A comparison between VHDL and verilog hardware description languages.
\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\\n"}]',published:!0,mainMedia:{caption:"Highly Cited",originalUrl:"/media/original/117"}},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 191 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 261 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
Note: Edited in March 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"6377",leadTitle:null,fullTitle:"Plant, Abiotic Stress and Responses to Climate Change",title:"Plant, Abiotic Stress and Responses to Climate Change",subtitle:null,reviewType:"peer-reviewed",abstract:"Climate change is a serious problem influencing agricultural production worldwide and challenging researchers to investigate plant responses and to breed crops for the changed growing conditions. Abiotic stresses are the most important for crop production, affecting about 96.5% of arable land worldwide. These stress factors include high and low temperature, water deficit (drought) and flooding, salinity, heavy metals, UV radiation, light, chemical pollutants, and so on. Since some of the stresses occurred simultaneously, such as heat and water deficit, causing the interactions of physiological processes, novel multidisciplinary solutions are needed. This book provides an overview of the present state in the research of abiotic stresses and molecular, biochemical, and whole plant responses, helping to prevent the negative impact of global climate change.",isbn:"978-1-78923-123-6",printIsbn:"978-1-78923-122-9",pdfIsbn:"978-1-83881-440-3",doi:"10.5772/intechopen.69916",price:119,priceEur:129,priceUsd:155,slug:"plant-abiotic-stress-and-responses-to-climate-change",numberOfPages:186,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"bfa5cbd0ee1ca1d9060ff51fd9f6468d",bookSignature:"Violeta Andjelkovic",publishedDate:"May 23rd 2018",coverURL:"https://cdn.intechopen.com/books/images_new/6377.jpg",numberOfDownloads:11060,numberOfWosCitations:35,numberOfCrossrefCitations:31,numberOfCrossrefCitationsByBook:1,numberOfDimensionsCitations:65,numberOfDimensionsCitationsByBook:1,hasAltmetrics:1,numberOfTotalCitations:131,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"June 14th 2017",dateEndSecondStepPublish:"July 5th 2017",dateEndThirdStepPublish:"October 1st 2017",dateEndFourthStepPublish:"December 30th 2017",dateEndFifthStepPublish:"February 28th 2018",currentStepOfPublishingProcess:5,indexedIn:"1,2,3,4,5,6",editedByType:"Edited by",kuFlag:!1,featuredMarkup:null,editors:[{id:"104331",title:"Dr.",name:"Violeta",middleName:null,surname:"Andjelkovic",slug:"violeta-andjelkovic",fullName:"Violeta Andjelkovic",profilePictureURL:"https://mts.intechopen.com/storage/users/104331/images/5663_n.jpg",biography:"Violeta Andjelkovic, PhD, is a head of Maize gene bank and manager of the European maize database at Maize Research Institute Zemun Polje, Serbia. As a post-doctoral researcher at Max-Planck Institute, Colone, Germany she worked on the expression analysis of genes induced by abiotic stresses in maize. She has coauthored more than 200 publications, and participated in numerous national and bilateral projects. Her actual research interest is focused on physiological and molecular aspects of abiotic stress, particularly drought, and on improvement of drought tolerance and grain quality in maize by utilization of genetic resources.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:null}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"311",title:"Plant Genetics",slug:"agronomy-plant-genetics"}],chapters:[{id:"60032",title:"Introductory Chapter: Climate Changes and Abiotic Stress in Plants",doi:"10.5772/intechopen.76102",slug:"introductory-chapter-climate-changes-and-abiotic-stress-in-plants",totalDownloads:1213,totalCrossrefCites:3,totalDimensionsCites:8,hasAltmetrics:0,abstract:null,signatures:"Violeta Andjelkovic",downloadPdfUrl:"/chapter/pdf-download/60032",previewPdfUrl:"/chapter/pdf-preview/60032",authors:[{id:"104331",title:"Dr.",name:"Violeta",surname:"Andjelkovic",slug:"violeta-andjelkovic",fullName:"Violeta Andjelkovic"}],corrections:null},{id:"58553",title:"Water Stress: Morphological and Anatomical Changes in Soybean (Glycine max L.) Plants",doi:"10.5772/intechopen.72899",slug:"water-stress-morphological-and-anatomical-changes-in-soybean-glycine-max-l-plants",totalDownloads:1842,totalCrossrefCites:9,totalDimensionsCites:17,hasAltmetrics:0,abstract:"Water stress is one of the most important physiological stress factors that adversely affect soybeans in many critical aspects of their growth and metabolism. Soybean’s growth, development and productivity are severely diminished, when soil or cell water potential becomes inadequate to sustain metabolic functioning. However, little has been done to gather comprehensive information regarding the specific changes that occur in water-stressed plants at the anatomical and morphological level. In this study, deviations in root growth, shoot growth, stomatal conductance, yield components and anatomical features are reported. Treatments with two levels of water stress imposed by reducing irrigation (once in 7 days or once in 15 days) revealed that, all cultivars (Dundee, LS 677, LS 678, TGx 1740-2F, TGx 1835-10E and Peking) were highly susceptible to prolonged water stress, exhibiting severe dehydration and death. A 15.0 and 30.0% survival frequency was obtained in plants irrigated once in 7 days; LS 677 and Peking, respectively. Unlike many other stresses, water deficit did not only affect the density of stomata, but, photosynthesis was affected by the lower levels of tissue CO2. These results suggest that, balanced biochemical, physiological, anatomical and morphological regulations are necessary for increased growth and yields in soybean.",signatures:"Phetole Mangena",downloadPdfUrl:"/chapter/pdf-download/58553",previewPdfUrl:"/chapter/pdf-preview/58553",authors:[{id:"191391",title:"Mr.",name:"Phetole",surname:"Mangena",slug:"phetole-mangena",fullName:"Phetole Mangena"}],corrections:null},{id:"58038",title:"Adaptation to Water Stress in Soybean: Morphology to Genetics",doi:"10.5772/intechopen.72229",slug:"adaptation-to-water-stress-in-soybean-morphology-to-genetics",totalDownloads:1787,totalCrossrefCites:5,totalDimensionsCites:12,hasAltmetrics:0,abstract:"Soybean (Glycine max L.) is the most important legume and oilseed crop. As a leguminous crop, it plays an irreplaceable role towards the sustainable agricultural system with biological nitrogen fixation. However, its production can be dramatically decreased by the occurrence of water stress. Water stress including drought and flooding induces the morpho-physiological and biochemical changes at different growth stages, which negatively affects the adaptability and yield of soybean. Genetic diversity that ensures productivity in challenging environment exists within germplasm, their wild relatives and species that are adapted to the water stress. The discovery of gene mapping, QTLs associated with root traits, slow canopy wilting, nitrogen fixation and flooding tolerance have accomplished significant progress in breeding programs. Identification of drought-responsive genes and transcription factors such as WRKY, DREBs, ERFs, ZIP, ZFP, MYB and NAC are valuable to ameliorate the water stress in soybean. Understanding the genetic mechanism using transcriptomic and proteomic approaches would be the ultimate choice for mitigating the water stress. Integration of well-designed soybean breeding program coupled with omic techniques would pave the way for developing drought and flooding resilient soybean cultivars.",signatures:"Tuanjie Zhao, Muqadas Aleem and Ripa Akter Sharmin",downloadPdfUrl:"/chapter/pdf-download/58038",previewPdfUrl:"/chapter/pdf-preview/58038",authors:[{id:"215701",title:"Ms.",name:"Muqadas",surname:"Aleem",slug:"muqadas-aleem",fullName:"Muqadas Aleem"},{id:"215702",title:"Dr.",name:null,surname:"Zhao",slug:"zhao",fullName:"Zhao"},{id:"225883",title:"Mrs.",name:"Ripa Akter",surname:"Sharmin",slug:"ripa-akter-sharmin",fullName:"Ripa Akter Sharmin"}],corrections:null},{id:"58166",title:"Sugar Beet Tolerance to Drought: Physiological and Molecular Aspects",doi:"10.5772/intechopen.72253",slug:"sugar-beet-tolerance-to-drought-physiological-and-molecular-aspects",totalDownloads:1177,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:0,abstract:"Drought often reduces sugar beet yield in the Balkan agroecological region. Climate forecasts indicate that this negative trend of drought periods will continue. Tolerance to drought is a complex trait, which comprises involvement of both physiological and molecular mechanisms in plants. This research was conducted on 11 sugar beet genotypes, which showed different tolerance to drought in the field. Experiment had three parts: water deficiency caused by cessation of watering conducted in the greenhouse, water deficiency imposed by different concentrations of polyethylene glycol on plants grown in tissue culture, and analysis of alterations in gene expression under drought. Plants exposed to stress in greenhouse had on average three leaves less, 4% lower water content, and seven-fold higher proline content. Classification of genotypes with respect to the level of tolerance to water deficiency on the basis of concentration of free proline, assessed in the experiment in vitro, corresponded to the result of the observation test in the field. Changes in the expression of candidate genes under drought suggest that one of them might be used for further development as a DNA-based marker. These results can be applied in sugar beet breeding aimed at increasing tolerance to water deficiency.",signatures:"Marina Putnik-Delić, Ivana Maksimović, Nevena Nagl and Branislava\nLalić",downloadPdfUrl:"/chapter/pdf-download/58166",previewPdfUrl:"/chapter/pdf-preview/58166",authors:[{id:"80173",title:"Prof.",name:"Ivana",surname:"Maksimovic",slug:"ivana-maksimovic",fullName:"Ivana Maksimovic"},{id:"214521",title:"Prof.",name:"Marina",surname:"Putnik-Delic",slug:"marina-putnik-delic",fullName:"Marina Putnik-Delic"},{id:"227506",title:"Dr.",name:"Nevena",surname:"Nagl",slug:"nevena-nagl",fullName:"Nevena Nagl"},{id:"227507",title:"Prof.",name:"Branislava",surname:"Lalić",slug:"branislava-lalic",fullName:"Branislava Lalić"}],corrections:null},{id:"58418",title:"Transcriptome, Genetic Transformation and Micropropagation: Some Biotechnology Strategies to Diminish Water Stress Caused by Climate Change in Sugarcane",doi:"10.5772/intechopen.72438",slug:"transcriptome-genetic-transformation-and-micropropagation-some-biotechnology-strategies-to-diminish-",totalDownloads:1024,totalCrossrefCites:0,totalDimensionsCites:1,hasAltmetrics:0,abstract:"Global climate change caused by natural processes results in major environmental issues that affect the world. Climate variability results in changes that cause water stress in plants. Sugarcane is a tropical grass C4, perennial and a multi-purpose industrial cash crop which serves as the main source of raw material for the production of sugar and biofuel. Farmers face the challenge to provide biotech alternatives with potential benefits and minimize potential adverse impacts on sugarcane’s production. In order to find biotechnology strategies to diminish the impact of climate change, our laboratory teamworks with micropropagation, transcriptome and genetic transformation of sugarcane using the var. MEX69290. In the transcriptome of sugarcane, a total of 536 and 750 genes were differentially regulated under normal and water stress treatment respectively, of which key genes were selected to be inserted into sugarcane for tolerance to abiotic stress. Regarding results of micropropagation, it was concluded that the continuous immersion propagation system was the best culture strategy. This may be as result of the elimination of gelling agent, which additionally helps reduce production costs.",signatures:"Evelyn Arlette Carrillo Bermejo, Miguel Angel Herrera Alamillo,\nSamuel David Gamboa Tuz, Miguel Angel Keb Llanes, Enrique\nCastaño de la Serna, Luis Manuel Robert Díaz and Luis Carlos\nRodríguez Zapata",downloadPdfUrl:"/chapter/pdf-download/58418",previewPdfUrl:"/chapter/pdf-preview/58418",authors:[{id:"62818",title:"Dr.",name:"Enrique",surname:"Castaño",slug:"enrique-castaao",fullName:"Enrique Castaño"},{id:"154564",title:"Dr.",name:"Luis",surname:"Rodriguez-Zapata",slug:"luis-rodriguez-zapata",fullName:"Luis Rodriguez-Zapata"},{id:"221080",title:"MSc.",name:"Samuel",surname:"Gamboa-Tuz",slug:"samuel-gamboa-tuz",fullName:"Samuel Gamboa-Tuz"},{id:"221083",title:"BSc.",name:"Evelyn Arlette",surname:"Carrillo-Bermejo",slug:"evelyn-arlette-carrillo-bermejo",fullName:"Evelyn Arlette Carrillo-Bermejo"},{id:"221087",title:"MSc.",name:"Miguel Ángel",surname:"Herrera-Alamillo",slug:"miguel-angel-herrera-alamillo",fullName:"Miguel Ángel Herrera-Alamillo"},{id:"221088",title:"Dr.",name:"Manuel L.",surname:"Robert",slug:"manuel-l.-robert",fullName:"Manuel L. Robert"},{id:"226794",title:"MSc.",name:"Miguel A.",surname:"Keb-Llanes",slug:"miguel-a.-keb-llanes",fullName:"Miguel A. Keb-Llanes"}],corrections:null},{id:"57908",title:"Plant Metabolomics in a Changing World: Metabolite Responses to Abiotic Stress Combinations",doi:"10.5772/intechopen.71769",slug:"plant-metabolomics-in-a-changing-world-metabolite-responses-to-abiotic-stress-combinations",totalDownloads:1403,totalCrossrefCites:7,totalDimensionsCites:13,hasAltmetrics:0,abstract:"Climate change constitutes a real threat to the global landscape. Current climate models predict an increased occurrence of coastal floods associated to sea level rise and long-term droughts associated to changes in the intra- and inter-year rainfall variability. Under natural environmental conditions, plants are routinely exposed to abiotic stresses, and must develop different strategies to cope with this multitude of climate change factors. Mass spectrometry (MS)-based plant metabolomics approaches are finding an increasing number of applications to investigate the molecular and biochemical mechanisms that underlie plant responses to changing environments. These studies provide a promising basis for facilitating our understanding of the plant’s flexibility to reconfigure central metabolic pathways (i.e., carbon, nitrogen and energy metabolism) as well as the degree by which plants tolerate and/or are susceptible to a climate change scenario. In this chapter, we will provide an update on the recent MS-based metabolomics strategies to study plant responses to drought, salt and heat stress as well as combinations thereof. We will describe how these stresses activate and coordinate several different signalling pathways, for example, through the synthesis of osmolytes.",signatures:"Tiago F. Jorge and Carla António",downloadPdfUrl:"/chapter/pdf-download/57908",previewPdfUrl:"/chapter/pdf-preview/57908",authors:[{id:"214540",title:"Dr.",name:"Carla",surname:"António",slug:"carla-antonio",fullName:"Carla António"},{id:"226332",title:"Mr.",name:"Tiago F.",surname:"Jorge",slug:"tiago-f.-jorge",fullName:"Tiago F. Jorge"}],corrections:null},{id:"59368",title:"Integrated Chemical Control of Abiotic Stress Tolerance Using Biostimulants",doi:"10.5772/intechopen.74214",slug:"integrated-chemical-control-of-abiotic-stress-tolerance-using-biostimulants",totalDownloads:1310,totalCrossrefCites:5,totalDimensionsCites:10,hasAltmetrics:1,abstract:"Given the high sensitivity of plants to environmental stress, the extreme environmental conditions derived from global climate change are now leading to a risk of decreases in crop production. The use of biostimulants, which enhance stress tolerance in plants, in combination with more traditional countermeasures, such as fertilizer application and irrigation, has significant potential to overcome stress-derived impacts on crops. In this review, the reasons for the inherent sensitivity of plants to environmental stress and the effects of biostimulants on enhancing stress tolerance are introduced. The availability of methods of integrated chemical control for improving crop production in the context of environmental stress is also discussed.",signatures:"Yasuo Yamauchi",downloadPdfUrl:"/chapter/pdf-download/59368",previewPdfUrl:"/chapter/pdf-preview/59368",authors:[{id:"111926",title:"Dr.",name:"Yasuo",surname:"Yamauchi",slug:"yasuo-yamauchi",fullName:"Yasuo Yamauchi"}],corrections:null},{id:"57871",title:"Tolerance-Induction Techniques and Agronomical Practices to Mitigate Stress in Extensive Crops and Vegetables",doi:"10.5772/intechopen.71771",slug:"tolerance-induction-techniques-and-agronomical-practices-to-mitigate-stress-in-extensive-crops-and-v",totalDownloads:1305,totalCrossrefCites:1,totalDimensionsCites:2,hasAltmetrics:1,abstract:"Environmental stress has regulated the function, morphology, and diversity of cells, organs, individuals and plant communities. The interaction of plants with the stress-inducing environments has produced in the plants a set of adaptive responses that can be studied in different description scopes: from organelles and subcellular structures to the level of plant communities. When it occurs for short time or low intensity, environmental stress can induce hardening, followed by induction of tolerance; on the other hand, when the plant´s reaction is for a long time or responding to a significant stress intensity, the response of plants includes decreased growth, depletion of metabolic reserves and loss of productivity and yield, even reaching the death of plants. Current knowledge about these crop responses can be translated into agronomic practices aimed at mitigating the adverse effects of environmental stress. This chapter will present the mechanisms of response and adaptation of crop plants to the environmental factors that most commonly cause crop damage or yield loss: high and low temperature, salinity, water deficit and nutrient deficits. Agronomic practices aimed at modifying or balancing some of the environmental factors involved and the use of tolerance induction techniques are described.",signatures:"Hipólito Hernández-Hernández, Fabián Pérez-Labrada, Ema Laura\nGarcía Enciso, Paola Leija-Martínez, Mari Carmen López-Pérez, Julia\nMedrano-Macías, Susana González-Morales, Antonio Juárez\nMaldonado, Luis Rubén García Dávila and Adalberto Benavides\nMendoza",downloadPdfUrl:"/chapter/pdf-download/57871",previewPdfUrl:"/chapter/pdf-preview/57871",authors:[{id:"213224",title:"MSc.",name:"Ema Laura",surname:"García Enciso",slug:"ema-laura-garcia-enciso",fullName:"Ema Laura García Enciso"},{id:"213225",title:"Dr.",name:"Adalberto",surname:"Benavides Mendoza",slug:"adalberto-benavides-mendoza",fullName:"Adalberto Benavides Mendoza"},{id:"213229",title:"Dr.",name:"Antonio",surname:"Juarez Maldonado",slug:"antonio-juarez-maldonado",fullName:"Antonio Juarez Maldonado"},{id:"214608",title:"Dr.",name:"Hipólito",surname:"Hernández-Hernández",slug:"hipolito-hernandez-hernandez",fullName:"Hipólito Hernández-Hernández"},{id:"214609",title:"Mr.",name:"Fabián",surname:"Pérez-Labrada",slug:"fabian-perez-labrada",fullName:"Fabián Pérez-Labrada"},{id:"214610",title:"Ms.",name:"Paola",surname:"Leija-Martínez",slug:"paola-leija-martinez",fullName:"Paola Leija-Martínez"},{id:"214611",title:"Ms.",name:"Mari Carmen",surname:"López-Pérez",slug:"mari-carmen-lopez-perez",fullName:"Mari Carmen López-Pérez"},{id:"214612",title:"Mrs.",name:"Julia",surname:"Medrano-Macías",slug:"julia-medrano-macias",fullName:"Julia Medrano-Macías"},{id:"214613",title:"Dr.",name:"Susana",surname:"González-Morales",slug:"susana-gonzalez-morales",fullName:"Susana González-Morales"},{id:"223868",title:"Mr.",name:"Luis Rubén",surname:"García-Dávila",slug:"luis-ruben-garcia-davila",fullName:"Luis Rubén García-Dávila"}],corrections:null}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},subseries:null,tags:null},relatedBooks:[{type:"book",id:"3060",title:"Plant Breeding from Laboratories to Fields",subtitle:null,isOpenForSubmission:!1,hash:"5b517f307caac739435f7fbaed5326ac",slug:"plant-breeding-from-laboratories-to-fields",bookSignature:"Sven Bode Andersen",coverURL:"https://cdn.intechopen.com/books/images_new/3060.jpg",editedByType:"Edited by",editors:[{id:"79388",title:"Prof.",name:"Sven Bode",surname:"Andersen",slug:"sven-bode-andersen",fullName:"Sven Bode Andersen"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3554",title:"Rice",subtitle:"Germplasm, Genetics and Improvement",isOpenForSubmission:!1,hash:"4bd6a333920f0f208c44c2e9fbfdd215",slug:"rice-germplasm-genetics-and-improvement",bookSignature:"Wengui Yan and Jinsong Bao",coverURL:"https://cdn.intechopen.com/books/images_new/3554.jpg",editedByType:"Edited by",editors:[{id:"94348",title:"Dr.",name:"Wengui",surname:"Yan",slug:"wengui-yan",fullName:"Wengui Yan"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"1404",title:"Aflatoxins",subtitle:"Detection, Measurement and Control",isOpenForSubmission:!1,hash:"e3a2b9bd1c46dd47875d6a0f3d8b2a39",slug:"aflatoxins-detection-measurement-and-control",bookSignature:"Irineo Torres-Pacheco",coverURL:"https://cdn.intechopen.com/books/images_new/1404.jpg",editedByType:"Edited by",editors:[{id:"62984",title:"Dr.",name:"Irineo",surname:"Torres-Pacheco",slug:"irineo-torres-pacheco",fullName:"Irineo Torres-Pacheco"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"2079",title:"Problems, Perspectives and Challenges of Agricultural Water Management",subtitle:null,isOpenForSubmission:!1,hash:"183bb777195754e887da67131255661f",slug:"problems-perspectives-and-challenges-of-agricultural-water-management",bookSignature:"Manish Kumar",coverURL:"https://cdn.intechopen.com/books/images_new/2079.jpg",editedByType:"Edited by",editors:[{id:"102967",title:"Dr.",name:"Manish",surname:"Kumar",slug:"manish-kumar",fullName:"Manish Kumar"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"597",title:"Crop Production Technologies",subtitle:null,isOpenForSubmission:!1,hash:"7f87c31dfd7e38f3e10cf7ec02df2201",slug:"crop-production-technologies",bookSignature:"Peeyush Sharma and Vikas Abrol",coverURL:"https://cdn.intechopen.com/books/images_new/597.jpg",editedByType:"Edited by",editors:[{id:"73200",title:"Dr.",name:"Peeyush",surname:"Sharma",slug:"peeyush-sharma",fullName:"Peeyush 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Microfluidics and Nanofluidics deal with transport phenomena, i.e., mass, momentum, and heat transfer, in the micrometer or nanometer range, and conventional fluid dynamics cannot be directly used in this area. The possible challenge in fluid properties must be considered. Thus, in the last two decades, the fundamental theories, as well as their application, have been rapidly in Microfluidics and Nanofluidics. Therefore, this book aims to host original research or review works addressing the fundamentals and applications of any functional flow in Microfluidics and Nanofluidics. The potential topics include all aspects of microfluidics, nanofluidics, and lab-on-a-chip science and technology, it might be (but are not limited to) fundamental principles of micro-and nanoscale phenomena like flow, mass transport and reactions, theoretical models, and numerical simulation with experimental and/or analytical proof, micromixer device, and particle manipulation. 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This chapter describes the application of Evolutionary Computation to the task of digital circuit design. Although many Electronic Design Automation (EDA) tools exist to aid designers, digital circuit design remains a time consuming and expensive task that requires skilled engineers.
The cost of errors in silicon is enormous and this has led to extremely powerful and accurate simulators that designers use to verify their designs before committing them to silicon. These simulators provide a huge opportunity for Evolutionary Computation as they can be used to test individuals.
This chapter gives an overview about how digital integrated circuits are designed and how the tools used to develop them have evolved over the past few decades. These tools, when linked together with GE produce a system we call the Automatic Design of Digital Circuits (ADDC), which can evolve circuits using massive levels of abstraction rather than simple logic gates.
We demonstrate the system on three real-world problems, including one with
Digital circuit design began in the 1960’s with the arrival of semiconductor transistor based circuits and the Integrated Circuit (IC). Up until the 2010’s,
Integrated circuits come in three different varieties; Digital, Analogue or Mixed-Signal. Digital integrated circuits process digital information, often represented using bits, bytes or words. Many of these circuits employ the use of one or more processors (often referred to as a core) with support logic, memories and I/O interfaces. The microprocessor is a famous example of a digital circuit. Analogue integrated circuits are used for handling continuous-time signals and to perform operations such as amplification, analogue filtering and power management. Mixed-Signal integrated circuits contain both analogue and digital circuitry in the same package and use ADC (Analogue to Digital Converters) and DACs (Digital to Analogue Converters) to share information between both domains.
In modern circuit design, signal processing tends to be performed in the digital domain instead of the analogue domain. This is due to the reliability of digital circuitry and the existence of advanced digital algorithms with performance that cannot be obtained with analog circuitry alone [1]. This move towards using digital designs for signal processing has required the use of circuit representations like Hardware Description Languages (HDLs) to be used to describe these extremely complex circuits. New devices such as Complex Programmable Logic Devices (CPLDs) and Field Programmable Gate Arrays (FPGAs) are increasingly being used due to their ability to replicate the behaviour of these circuits without requiring the fabrication of new chips. The following sections will go more in-depth into HDLs, the differences between CPLD and FPGA devices and an overview of the Digital Design Flow.
The first modern HDL, Very High Speed Integrated Circuit Hardware Description Language (VHSIC-HDL), more commonly known as VHDL, was created in 1983. VHDL was developed for the US Department of Defense as part of the VHSIC project. The project was launched in 1980 [2], while the first version of VHDL was launched in 1983 by Intermetrics Inc., Texas Instruments and IBM [3, 4]. VHDL is a verbose and strongly-typed language. It grew steadily in popularity, resulting in both logic simulators and logic synthesis tools being developed for it. IEEE Standard VHDL was standardised in 1986 [5] with the adoption of VHDL version 7.2 and was finalised in 1987 in the IEEE Standard 1076-1987 [5]. VHDL would become the first HDL language that would gain widespread adoption, and is still in use today.
Another modern HDL developed around this time was Verilog, created by Phil Moorby in 1983 [6] while working for Gateway Design Automation, who were acquired by Cadence Design Systems in 1989 [7]. In comparison to VHDL, Verilog is less verbose and is a weakly-typed language. Originally it was designed only for logic simulation, but later had logic synthesis features added after the language gained widespread popularity. Verilog-XL, a Verilog simulator owned by Cadence, became the
VHDL | SystemVerilog |
---|---|
Standardised in 1987 | Standardised in 1995 (Verilog) and 2005 (SystemVerilog) |
More Verbose | Less Verbose |
ADA-like | C-like |
Case Insensitive | Case Sensitive |
Support for Digital, Analog and Mixed-Signal Designs | Support for Digital Designs only |
A comparison between VHDL and verilog hardware description languages.
With the introduction of Hardware Description Languages for digital circuit design, two discrete time based simulation methods came into prominence. Both cycle-driven and event-driven simulation methods were orders of magnitude faster than the traditional continuous time based simulation method “SPICE”. One limitation of the cycle-driven simulation method is that the output is only updated on each clock edge This means it can only be used for synchronous digital designs, but is much faster than event-driven simulation. It also cannot detect glitches and doesn’t take the timing of the design into consideration.
Event-driven simulation updates the output on any input event meaning it can be used for both synchronous and asynchronous designs. Although still quicker than SPICE methods, it is much slower than cycle-driven simulation. Modern circuit designs utilise techniques such as clock and power gating, allowing parts of a design to be “turned off”. This can help reduce the simulation time of an event-driven simulation, bringing it closer to cycle-driven simulation while providing a more accurate simulation. Table 2 provides a comparison between cycle-driven and event-driven simulation methods. Practically all commercial and open-source simulation tools today utilise one of these methods.
Cycle-driven simulation | Event-driven simulation |
---|---|
Evaluation every clock cycle | Evaluation at minimum time-step or greater |
Synchronous Designs only | Synchronous and Asynchronous Designs |
Behavioural Simulation only | Behavioural, Functional and Timing Simulations |
Faster Simulation Speed | Slower Simulation Speed |
A comparison between cycle-driven simulation and event-driven simulation.
As digital designs grew in complexity, early Programmable Logic Devices (PLD) such as Programmable Array Logic (PAL) became obsolete as they could only replicate the behaviour of a few hundred logic gates. To address this shortcoming, PALs were soon replaced by Complex Programmable Logic Devices (CPLD). Modern CPLDs are able to replicate the behaviour of hundreds of thousands of logic gates. One advantage of CPLD devices is that they use non-volatile memory to store their configuration. As a result, their logic is already configured at power-up. This makes them ideal devices for systems where the logic is required to be ready for initialisation, such as glue logic for circuits.
Figure 1 shows the internal structure of a CPLD. These logic blocks consist of programmable PAL blocks. The inputs can be connected together to different AND gates using programmable fuses. The OR gate connections are fixed and cannot be reconfigured. Although less configurable than a PLA (which contains both programmable AND and OR planes), this reduction in complexity makes PAL blocks cheaper to manufacture. In order for PAL blocks to be able to implement sequential designs, a D flip-flop can be used to store the state of the output. CPLDs can connect multiple logic blocks together using the programmable interconnection matrix in order to implement more complex designs.
Structure of a Complex Programmable Logic Device (CPLD) and Programmable Array Logic (PAL) block. The programmable AND plane and the fixed OR plane are shown on the right.
While CPLD devices are still used for specific tasks, the most common PLD in use today is the Field Programmable Gate Array (FPGA). These devices are quite similar in structure to the mask-programmed gate array (MPGA) [11] which was one of the first commercial programmable PLDs available. One benefit of using FPGAs is that they can be electronically reconfigured, whereas the previous MPGAs configuration was specified at the time of manufacture. The first FPGA, the Xilinx XC2064 was invented by Ross Freeman and Bernard Vonderschmitt in 1985 [12]. Early FPGAs were mainly used in the telecommunications and networking sectors as they were often cheaper than manufacturing custom silicon for these tasks.
Figure 2 shows the internal structure of the FPGA. Similarities can be seen between FPGA and CPLD devices where a programmable interconnect is used to connect programmable logic blocks. In an FPGA, the Configurable Logic Blocks (CLB) consist of Look Up Tables (LUTs). The output of these programmable memories are defined by their input signals. The multiplexer then selects either the output of the LUT or the D flip flop to allow for combinational or sequential logic, similar to PAL blocks in CPLDs. These blocks can then be connected together using the Switching Blocks (SB). Modern FPGAs are able to replicate the behaviour of tens of millions of logic gates and contain logic like RAM and multipliers. Today, they are often used in high-performance computing applications due to their performance and efficiency over processor-based algorithms.
Structure of a Field Programmable Gate Array (FPGA). The Complex Logic Block (CLB) consists of a programmable memory called a Look Up Table (LUT), a D flip flop to store state and a multiplexer to select the output signal. The programmable Switching Block (SB) is used to connect the CLBs together.
In digital design, it is often not practical to use gate-level descriptions. Instead, a representation called Register Transfer Level (RTL) is used. RTL allows for a high-level model of the design to be represented without having to think about the low-level logic structures required to implement the functionality [13]. This abstraction uses constructs like logic statements, arithmetic operations and control flow. Similarities can be drawn between programming using mnemonics in Assembly Language compared to functional programming in C. Using RTL allows the designer to focus on the functionality of the design rather than on the implementation. Figure 3 presents the different stages a digital design must goes through in order to convert a RTL representation into an implementable design.
A flowchart showing the different stages of the logic synthesis/digital design process.
When a high level language is used for programming, the code written by the programmer must first go through a process called compilation before the code is executed. Similarly with digital hardware, a design specified using RTL (often using a HDL like VHDL or SystemVerilog) must go through a process called logic synthesis. This process analyses the given RTL and converts it into a set of primitives that is functionally equivalent. Primitives are the basic building blocks of any logic design and consists of both combinational and sequential blocks. Examples include boolean logic (NOT/AND/OR/XOR etc.), multiplexers and flip-flops. This output is stored in a file called a net-list, which contains a list of all the primitive blocks and the nets that connect them together.
The net-list generated from the logic synthesis is not optimized and must go through a process known as logic minimization. There can be many parameters to optimize for, such as area usage, power consumption and timing delay. There are many different methods that can be used to perform this optimization. Some early algorithmic methods include Karnaugh maps [13] and the Quine–McCluskey algorithm [14]. However as designs have become increasing complex, these algorithmic methods are not computationally feasible. This has lead to the use of heuristic optimizers such as ESPRESSO [15] and BOOM [16]. When using heuristic optimizers, it cannot be guaranteed that the minimized design is the global minimum. However in practice, these methods are sufficient and are widely used in logic synthesis tools today.
Following the logic minimization process, the optimized net-list is in an intermediate representation. A process called Technology Mapping must be performed before the design can be implemented in silicon or on a PLD. For silicon, this intermediate representation is compared against a library of available “building blocks” called leaf-level cells. The mapper then selects and connects these leaf-level cells, rebuilding the circuit. Further optimization may be performed here as the available leaf cells may be able to replace multiple blocks in the intermediate representation. For PLDs, the process is similar. The PAL blocks in CPLDs and the LUTs in FPGAs can be configured to replace one or multiple blocks. These are then connected together using the programmable interconnects. In comparison to logic minimization, the optimizations performed here are much simpler. After the technology mapping process is complete, the designer now has an implementable design. This is often in the GDSII/OASIS format for silicon manufacturing and in a bitstream format for PLDs. The top EDA companies for ASIC digital design tools include Cadence Design Systems, Siemens and Synopsys, with Xilinx and Intel providing FPGA tools.
Grammatical Evolution (GE), the tool used in this chapter and described in detail in the next section, has been used to evolve Verilog circuits, such as the one-bit adder and D-type latch at the gate level [17]. Notably, the one-bit adder is frequently used as a case study to evolve combinational circuits at the gate-level through GE [18, 19, 20]. However, gate-level evolution is less likely to scale to highly complex circuits from scratch [21]. In response to scalability issues inherent in gate level evolution, [22] proposed functional level evolution through Genetic Algorithms, which uses higher-level functions such as multiplexers, adders, subtractors instead of primitive gates to help reduce the search space. Similarly, [23] evolved a 3-bit multiplier using only binary multiplexers. 9- and 25-Median approximate circuits have also been designed at the functional level through Cartesian GP [24]. We address the scalability concern by performing circuit evolution through GE at a more abstract level – RTL modeling, where the focus is on describing the circuit’s behavior [25, 26].
Biological evolution has been a source of inspiration for many techniques that formed the field of evolutionary computation (EC), and has been used to address a wide range of problem domains ranging from the small to the huge, solving molecular to astronomical related problems. One of the most successful evolutionary techniques is GP, introduced by John R. Koza in his book “Genetic Programming—On the programming of Computers by Means of Natural Selection” [27], which mimics natural selection in an iterative way to find an optimal (best) solution. Algorithm 1 details the steps required to implement a standard GP. A survey of the different GP techniques current available in the literature is out of the scope of this work, the interested reader can find in [28] a comprehensive review of various aspects and techniques of GP and their categorization.
Grammatical evolution (GE) is an evolutionary computation and, more specifically, a genetic programming (GP) technique [29] that addresses the closure issue of Koza-style GP, which effectively confines GP to single-type problems. This is achieved through the use of a grammar, generally in Backus-Naur Form (BNF) [29, 30], or Attribute Grammar (AG) [31, 32, 33, 34].
The GE system shown in Figure 4 automatically generates programs using three main components: (i) grammar; (ii) cost function; and (iii) search engine. The grammar describes the program’s syntax, the cost function evaluates the quality of each program, and the search engine, typically a GA, searches within the program space defined by the grammar.
The GE system uses a search engine (typically a GA) to generate solutions for a given problem, by recombining the genetic material (genotype) and mapped onto programs (phenotype) according to a language specification (interpreter/compiler).
In GE, a typical representation for an individual is a binary string grouped into codons (e.g. 8 bits). The linear representation of the genome allows the application of genetic operators such as crossover and mutation in the manner of a typical GA, unlike tree-based GP.
In GP, the standard initialisation is the ramped-half-and-half (RHH) technique, introduced in [27]. In order to ensure diversity in the population, GP individuals typically represented as trees are created with different depths. The RHH technique uses two methods to create a tree: full and grow. Typically there is a probability of 0.5 to select either method for a particular individual. The full method creates trees with full branches at the maximum specified depth, whereas the grow method creates trees with different length of branches and different depth size up to the maximum allowed.
Generally, GE uses a one-point crossover as it has been shown to be effective [36]. In crossover, two individuals are selected as parents and a single crossover point within each parent’s genome is randomly chosen, dividing the genome into two halves: left and right sub-genomes. The right sub-genomes of both parents are swapped to create two offspring. However, crossover points that lie within non-coding regions (unused codon(s) from the mapping step) may not be so useful. As a result, a variant of one-point crossover known as
To illustrate the application of GE, we first explain the evolutionary process using a mathematical optimisation problem as study case.
GE begins with the start symbol of the grammar, then the codons are used to select and apply the grammar production rules to finally build a program. This mapping process is illustrated in Figure 5 with a simple example, where the production rules in the grammar contains a set of user-defined functions:
Example of a GE genotype-phenotype mapping process for the Iris dataset, where the binary genotype is grouped into codons (e.g. 8 bits; red & blue), transcribed into an integer string, then used to select production rules from a predefined grammar (BNF-Grammar), and finally translated into a sequence of rules to build a solution (phenotype).
The production rules for each non-terminal are indexed starting from 0 and, when selecting a production rule (starting with the left-most non-terminal of the developing program) the next codon value in the genome is read and interpreted using the formula:
To prevent reaching the end of the genome without consuming all the available codons, then a wrapping process is used to continuing reading from the beginning of the genome. This mapping process stops when all of the non-terminal symbols have been replaced, in order to get a valid program. An exemption to this process is in the case when it fails to replace all of the non-terminal symbols after a maximum number of iterations, then it is considered an invalid individual and it is penalized with the lowest possible fitness.
ADDC is an evolutionary HDL circuit design framework mainly driven by GE. ADDC requires a grammar and a testbench as inputs for circuit evolution and verification respectively. The designed grammar must be BNF compliant and must satisfy the grammar sufficiency property. Thus, the grammar must contain all the necessary building blocks required to potentially evolve an optimal circuit. ADDC is technology agnostic and easily configurable as the choice of HDL and simulator are left to the user to choose. Illustrated in Figure 6 is ADDC’s design flow for functional evolution of circuits.
ADDC Functional Circuit Evolution Overview.
During the initial phase of the circuit design process, ADDC creates an initial population of circuit designs using a suitable GE initialisation routine such as sensible initialisation. These individuals then undergo fitness evaluation. The fitness evaluation phase entails a number of steps. First, the genotype (genome) of each individual is translated to a HDL (SystemVerilog in this work) circuit design (phenotype) by the GE mapper, using the grammar designed for the circuit. Functional simulation of each circuit takes place, assuming all circuit designs are valid. For these experiments,
The next phase is reproduction, where usually individuals with either good overall fitness score or individuals that perform best on certain cases are selected for crossover and mutation to create a new population of circuit designs. Lexicase selection performs well on circuit design benchmarks [25, 26], hence selected as the choice of selection routine. Also, depending on the genetic algorithm (GA) of choice, for example steady state, generational GAs etc., events like replacement or elitism may take place in creating the new population. The new population undergoes fitness evaluation in similar manner as described in the previous section. The process continues until the termination criterion is satisfied and the best circuit design returned as solution.
Three circuit benchmark problems are considered, namely:
Hamming codes are a linear error-correcting codes capable of detecting a single error and at most two errors, but are only capable of correcting a single error. They belong to a category of codes referred to as Linear Block Codes. A Hamming Code (7,4) Encoder encodes a 4-bit data word into a 7-bit code word prior to data transmission by generating and adding three parity bits to the data word.
The structure of the code words generated by hamming codes can be classified into two categories:
The grammar designed for evolving the Hamming Code (7,4) Encoder is shown in Figure 7. The circuit’s interface is defined using the
Hamming code (7,4) encoder grammar.
A Seven Segment Display is an electronic device used for the display of decimal numerals. It is also capable of displaying letters, though some letters such as
Seven segment display grammar.
A multiplexer is a multiple-input single-output device that accepts data (data lines) and an address (select lines) as inputs and uses the address to select the corresponding data line to be transmitted. The 16-to-4 multiplexer has 16 data lines and 4 select lines.
Figure 9 shows the grammar designed to evolve the multiplexer. Similar to the Seven Segment Display Grammar, the 16-to-4 Multiplexer Grammar also uses the always procedural block. However, here an
16-to-4 multiplexer grammar.
Experimental parameters used for running the experiments are shown in Table 3. The generation number and population size were selected based on preliminary experiments. The generation sizes used for the preliminary experiments were 50, 100 and 200; the population sizes were 100, 200, 500, 1000 and 2000. For each problem, 5 independent runs were conducted. The choice of generation number and population size for the actual experiments were based on setups with majority of the runs with mean best fitness of the final generation within the fourth quartile of the maximum fitness. The other parameters used remain the same as used in [25, 26].
Parameter | Value |
---|---|
Initialization | Sensible Initialization |
No of generations | 50 |
Mutation rate | |
Crossover rate | |
Replacement rate | |
No of independent runs | |
Population | 1,000 |
Selection | Lexicase Parent Selection |
Experimental run parameters.
50 generations were used for evolving the Hamming Code (7,4) Encoder, while 100 generations was used for each of the Seven Segment Display and 16-to-4 Multiplexer designs as preliminary results revealed these problems were relatively challenging to evolve compared to the Hamming Code (7,4) Encoder. All other parameters remain the same for all benchmark problems.
The number of training and testing cases are tabulated in Table 4.
Benchmark problem | No of training cases | No of testing cases |
---|---|---|
Hamming Code (7,4) Encoder | 112 | — |
Seven Segment Display ( | 16 | — |
16-to-4 Multiplexer | 4100 | 5000 |
Number of training and testing cases.
Each of the Hamming Code (7,4) Encoder and Seven Segment Display have only 16 cases. However, for the Hamming Code (7,4) Encoder every correct bit in each bit position in the codeword is counted as part of the total fitness score for a candidate circuit, giving a total of 112 (
On the other hand, the 16-to-4 Multiplexer has
Results obtained from experiments conducted demonstrate ADDC is ideal for evolving digital circuit designs due to the use GE and a HDL which permits designs to be done at a more abstract level. The evolutionary performance for the experiments conducted for Hamming Code (7,4) Encoder, Seven Segment Display and 16-to-4 Multiplexer described in Section 5.1 are visualized in Figures 10–12 respectively. The success rate per benchmark problem is tabulated in Table 5. A representative solution per each circuit benchmark problem is shown in Figures 13–15 in the Appendix. The advantages and disadvantages of the proposed approach is discussed in Section 5.7.
Mean best and mean average across runs for hamming code (7,4) encoder.
Mean best and mean average across runs for seven segment display.
Mean best and mean average across runs for 16-to-4 multiplexer.
Benchmark problem | Success rate |
---|---|
Hamming Code (7,4) Encoder | |
Seven Segment Display ( | |
16-to-4 Multiplexer |
Success rate for benchmark problems.
A successful run is a single independent evolutionary run that evolved an optimal circuit for the target problem. Fifty independent runs were conducted for all three benchmark problems. The success rate is the number of successful runs divided by the total number of evolutionary runs (i.e. 50) as tabulated in Table 5. A 100% success rate was attained for the Hamming Code (7,4) Encoder. The Seven Segment Display and 16-to-4 Multiplexer obtained 60 and 86% success rates, respectively.
Visualization of the evolutionary performance as evolution progressed for Hamming Code (7,4) Encoder, Seven Segment Display and 16-to-4 Multiplexer are shown in Figures 10–12 respectively.
The red line represents the mean best fitness per generations across the 50 independent runs conducted, while the black line represents the mean average fitness. Also plotted are error bars representing the standard error. The error bars are short to non-existent indicating small variability between the fitnesses of individuals. Furthermore, all three plots reveal a steady and progressive increase in fitness as the evolution progressed indicating the evolutionary search is continuously searching regions of the solutions where fitter individuals are located. Hamming Code (7,4) Encoder and 16-to-4 Multiplexer problems discover individual(s) that solve more that 50% of the test cases from the initial generations while the Seven Segment Display evolves individual(s) that solve 25% of the test cases on average.
First, evolved circuit designs are quite interpretable compared to gate-level designs. This is due to the high level of abstraction at which these designs are performed which focuses on evolving circuit behaviours as opposed to evolving gate-level designs. Gate-level design approaches are challenging to scale to complex circuits [40]. The use of constructs such as
Third, like any other methodology, there exist few disadvantages. The use of HDL requires the user to have technical knowledge about the HDL of choice— how to design grammars free of syntax errors and modelling errors. Syntax errors are easier to find and fix as most simulators will report such errors at the functional simulation phase. Modelling errors are a bit more challenging to fix, as they are only noticeable during synthesis (conversion of RTL or high level designs to gate-level representation) phase of the circuit design when designed grammars do not adhere to the guidelines of the HDL of choice. For example, fully functional representative solutions for the 16-to-4 Multiplexer and Seven Segment Display shown in Figures 13 and 14 respectively may not be directly synthesizable (depending on the synthesis tool), as the
The choice of operators to use for evolving circuits is key as it has been shown to increase simulation time of circuits if inappropriate operators are chosen [26]. Furthermore, some circuit designs may contain redundant block of code which impede interpretability as observed in representative best solutions for 16-to-4 Multiplexer and Seven Segment Display shown in Figures 13 and 14 respectively in Appendix. Only 16 of the if conditions and case statements are valid for the 16-to-4 Multiplexer and Seven Segment Display representation circuit designs respectively.
We have presented a system for the automated design of digital circuits, ADDC. ADDC is the next logical step in the evolution of Electronic Design Automation and this chapter has described how the history of integrated circuits has led to the confluence of GE, circuit simulators and HDLs. ADDC has been demonstrated on three difficult, real-world problems and was successful on all three of them, including one with
The HDLs employed here are hugely powerful and expressive. Digital designers often operate at very high levels of abstraction using
As the problems scale up, the number of test cases can become astronomical, as was the case in this chapter. While in this case we randomly sampled the training and test cases, it is also possible to use a more intelligent approach. Recent work [41] has investigated using clustering to select a representative set of test cases. This will permit us to operate at greater scales with confidence.
A circuit that functions correctly on a simulator is not guaranteed to be fit for purpose when rendered in silicon. This is because there are often other considerations, such as silicon area, power dissipation and delay. Future work will use multi-objective optimisation to include pressure on individuals to adhere to these constraints too.
While some of our automatically generated circuits have successfully been implemented in silicon on a Xilinx Artix-7 FPGA, e.g., an 8-to-1 multiplexer [25], ADDC does not yet include that step in its toolchain; to be fully automated it will need to include this.
The authors are supported by Research Grants 13/RC/2094 and 16/IA/4605 from the Science Foundation Ireland and by Lero, the Irish Software Engineering Research Centre (www.lero.ie). The third is partially financed by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brazil (CAPES), Finance Code 001, and Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ).
The authors declare no conflict of interest.
16-to-4 multiplexer representative solution.
Seven segment display representative solution.
Hamming code (7,3) encoder representative solution.
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