LAcc: Exploiting Lookup Table-based Fast and Accurate Vector Multiplication in DRAM-based CNN Accelerator

被引：39

作者：

Deng, Quan ^{[1
]}

Zhang, Youtao ^{[2
]}

Zhang, Minxuan ^{[1
]}

Yang, Jun ^{[3
]}

机构：

[1] Natl Univ Def Technol, Coll Comp, Changsha, Hunan, Peoples R China

[2] Univ Pittsburgh, Comp Sci Dept, Pittsburgh, PA 15260 USA

[3] Univ Pittsburgh, Elect & Comp Engn Dept, Pittsburgh, PA 15260 USA

来源：

PROCEEDINGS OF THE 2019 56TH ACM/EDAC/IEEE DESIGN AUTOMATION CONFERENCE (DAC) | 2019年

基金：

中国国家自然科学基金;

关键词：

D O I：

10.1145/3316781.3317845

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

PIM (Processing-in-memory)-based CNN (Convolutional neural network) accelerators leverage the characteristics of basic memory cells to enable simple logic and arithmetic operations so that the bandwidth constraint can be effectively alleviated. However, it remains a major challenge to support multiplication operations efficiently on PIM accelerators, in particular, DRAM-based PIM accelerators. This has prevented PIM-based accelerators from being immediately adopted for accurate CNN inference. In this paper, we propose LAcc, a DRAM-based PIM accelerator to support LUT-(lookup table) based fast and accurate multiplication. By enabling LUT based vector multiplication in DRAM, LAcc effectively decreases LUT size and improve its reuse. LAcc further adopts a hybrid mapping of weights and inputs to improve the hardware utilization rate. LAcc achieves 95 FPS at 5.3 W for Alexnet and 6.3. efficiency improvement over the state-of-the-art.

引用

页数：6