.png){kind=icon}
返回
Rethinking Embedded Blocks for Machine Learning Applications
.png){kind=icon}
PRE
AI
DOI：10.1145/3491234.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The underlying goal of FPGA architecture research is to devise flexible substrates that implement a wide variety of circuits efficiently. Contemporary FPGA architectures have been optimized to support networking, signal processing, and image processing applications through high-precision digital signal processing (DSP) blocks. The recent emergence of machine learning has created a new set of demands characterized by: (1) higher computational density and (2) low precision arithmetic requirements. With the goal of exploring this new design space in a methodical manner, we first propose a problem formulation involving computing nested loops over multiply-accumulate (MAC) operations, which covers many basic linear algebra primitives and standard deep neural network (DNN) kernels. A quantitative methodology for deriving efficient coarse-grained compute block architectures from benchmarks is then proposed together with a family of new embedded blocks, called MLBlocks. An MLBlock instance includes several multiply-accumulate units connected via a flexible routing, where each configuration performs a few parallel dot-products in a systolic array fashion. This architecture is parameterized with support for different data movements, reuse, and precisions, utilizing a columnar arrangement that is compatible with existing FPGA architectures. On synthetic benchmarks, we demonstrate that for 8-bit arithmetic, MLBlocks offer 6x improved performance over the commercial Xilinx DSP48E2 architecture with smaller area and delay; and for timemultiplexed 16-bit arithmetic, achieves 2x higher performance per area with the same area and frequency. All source codes and data, along with documents to reproduce all the results in this article, are available at http://github.com/raminrasoulinezhad/MLBlocks.
Keyword:
FPGA Architectures
coarse-grained compute blocks
reconfigurable architecture
neural networks
digital signal processing
期刊
IF:
2.8
论文数:
597
被引数:
810
机构
引用论文
分享收藏Nouveaux jouets : ce que les enfants identifient comme " jouets de garçons " et " jouets de filles ".png)
Enfance
EnfanceIF0
PREAI
分享收藏Perceived Motivating Factors and Barriers for the Completion of Postgraduate Training Among American Pharmacy Students Prior to Beginning Advanced Pharmacy Practice Experiences美国药学学生在开始高级药学实践经历之前，完成研究生培训的感知激励因素与障碍因素
OAAI
OAAI分享收藏Using the three e’s (emphasis, expectations, and evaluation) to structure writing objectives for pharmacy practice experiences运用“三E”原则（即重点、期望和评估）来构建药学实践体验的写作目标
PREAI
分享收藏Student perceptions of the knowledge, skills, and abilities desired by potential employers of psychology majors.心理学专业学生对潜在雇主所期望的知识、技能和能力的主观看法。
PREAI
分享收藏
查看更多内容