{"title":"LV: Latency-Versatile Floating-Point Engine for High-Performance Deep Neural Networks","authors":"Yun-Chen Lo;Yu-Chih Tsai;Ren-Shuo Liu","doi":"10.1109/LCA.2023.3287096","DOIUrl":null,"url":null,"abstract":"Computing latency is an important system metric for Deep Neural Networks (DNNs) accelerators. To reduce latency, this work proposes \n<bold>LV</b>\n, a latency-versatile floating-point engine (FP-PE), which contains the following key contributions: 1) an approximate bit-versatile multiplier-and-accumulate (BV-MAC) unit with early shifter and 2) an on-demand fixed-point-to-floating-point conversion (FXP2FP) unit. The extensive experimental results show that LV outperforms baseline FP-PE and redundancy-aware FP-PE by up to 2.12× and 1.3× speedup using TSMC 40-nm technology, achieving comparable accuracy on the ImageNet classification tasks.","PeriodicalId":51248,"journal":{"name":"IEEE Computer Architecture Letters","volume":"22 2","pages":"125-128"},"PeriodicalIF":1.4000,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Computer Architecture Letters","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10230255/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
Computing latency is an important system metric for Deep Neural Networks (DNNs) accelerators. To reduce latency, this work proposes
LV
, a latency-versatile floating-point engine (FP-PE), which contains the following key contributions: 1) an approximate bit-versatile multiplier-and-accumulate (BV-MAC) unit with early shifter and 2) an on-demand fixed-point-to-floating-point conversion (FXP2FP) unit. The extensive experimental results show that LV outperforms baseline FP-PE and redundancy-aware FP-PE by up to 2.12× and 1.3× speedup using TSMC 40-nm technology, achieving comparable accuracy on the ImageNet classification tasks.
期刊介绍:
IEEE Computer Architecture Letters is a rigorously peer-reviewed forum for publishing early, high-impact results in the areas of uni- and multiprocessor computer systems, computer architecture, microarchitecture, workload characterization, performance evaluation and simulation techniques, and power-aware computing. Submissions are welcomed on any topic in computer architecture, especially but not limited to: microprocessor and multiprocessor systems, microarchitecture and ILP processors, workload characterization, performance evaluation and simulation techniques, compiler-hardware and operating system-hardware interactions, interconnect architectures, memory and cache systems, power and thermal issues at the architecture level, I/O architectures and techniques, independent validation of previously published results, analysis of unsuccessful techniques, domain-specific processor architectures (e.g., embedded, graphics, network, etc.), real-time and high-availability architectures, reconfigurable systems.