C. PrashanthH., R. SoujanyaS., Bindu G. Gowda, M. Rao
{"title":"基于精确压缩器的近似乘法器设计与评价","authors":"C. PrashanthH., R. SoujanyaS., Bindu G. Gowda, M. Rao","doi":"10.1145/3526241.3530320","DOIUrl":null,"url":null,"abstract":"VLSI implementation of arithmetic functions are of high demand considering the rise in hardware realization of image and digital signal processing modules for various autonomous applications. The hardware implementation offers faster results and desirable outcome, but expecting the same design metrics in the form of power, footprint and delay on a tiny decision-making edge devices with limited resources needs design improvisation. Approximate computing promises to support the required hardware metrics in error resilient applications where the inexact output is not deviated much from the expected one, and decision made remains unchanged. Multiplier design blocks are heavily used in the multimedia functional chip, and introducing approximation in these blocks effectively benefits design metrics and chip cost of the developed system-on-chip(SoC). The proposed work attempts to design and use various sizes of approximate AND-OR re-coded compressors in the multiple reduction stages, along with various fast adders in the final addition stage of multiplier design. Further, design metrics and resources utilized for different multiplier designs were characterized in ASIC and FPGA synthesis flows respectively, along with their error statistics. Designed approximate multipliers were employed in Gaussian smoothing application to evaluate the quality-hardware resource trade-off of approximation","PeriodicalId":188228,"journal":{"name":"Proceedings of the Great Lakes Symposium on VLSI 2022","volume":"31 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Design and Evaluation of In-Exact Compressor based Approximate Multipliers\",\"authors\":\"C. PrashanthH., R. SoujanyaS., Bindu G. Gowda, M. Rao\",\"doi\":\"10.1145/3526241.3530320\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"VLSI implementation of arithmetic functions are of high demand considering the rise in hardware realization of image and digital signal processing modules for various autonomous applications. The hardware implementation offers faster results and desirable outcome, but expecting the same design metrics in the form of power, footprint and delay on a tiny decision-making edge devices with limited resources needs design improvisation. Approximate computing promises to support the required hardware metrics in error resilient applications where the inexact output is not deviated much from the expected one, and decision made remains unchanged. Multiplier design blocks are heavily used in the multimedia functional chip, and introducing approximation in these blocks effectively benefits design metrics and chip cost of the developed system-on-chip(SoC). The proposed work attempts to design and use various sizes of approximate AND-OR re-coded compressors in the multiple reduction stages, along with various fast adders in the final addition stage of multiplier design. Further, design metrics and resources utilized for different multiplier designs were characterized in ASIC and FPGA synthesis flows respectively, along with their error statistics. Designed approximate multipliers were employed in Gaussian smoothing application to evaluate the quality-hardware resource trade-off of approximation\",\"PeriodicalId\":188228,\"journal\":{\"name\":\"Proceedings of the Great Lakes Symposium on VLSI 2022\",\"volume\":\"31 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Great Lakes Symposium on VLSI 2022\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3526241.3530320\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Great Lakes Symposium on VLSI 2022","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3526241.3530320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design and Evaluation of In-Exact Compressor based Approximate Multipliers
VLSI implementation of arithmetic functions are of high demand considering the rise in hardware realization of image and digital signal processing modules for various autonomous applications. The hardware implementation offers faster results and desirable outcome, but expecting the same design metrics in the form of power, footprint and delay on a tiny decision-making edge devices with limited resources needs design improvisation. Approximate computing promises to support the required hardware metrics in error resilient applications where the inexact output is not deviated much from the expected one, and decision made remains unchanged. Multiplier design blocks are heavily used in the multimedia functional chip, and introducing approximation in these blocks effectively benefits design metrics and chip cost of the developed system-on-chip(SoC). The proposed work attempts to design and use various sizes of approximate AND-OR re-coded compressors in the multiple reduction stages, along with various fast adders in the final addition stage of multiplier design. Further, design metrics and resources utilized for different multiplier designs were characterized in ASIC and FPGA synthesis flows respectively, along with their error statistics. Designed approximate multipliers were employed in Gaussian smoothing application to evaluate the quality-hardware resource trade-off of approximation