NEURAL ACCELERATION FOR GENERAL-PURPOSE APPROXIMATE PROGRAMS

被引：24
|
作者：
Esmaeilzadeh, Hadi [1 ]
Sampson, Adrian [1 ]
Ceze, Luis [1 ]
Burger, Doug
机构：
[1] Univ Washington, Dept Comp Sci & Engn, Seattle, WA 98195 USA
来源：
IEEE MICRO | 2013年 / 33卷 / 03期
关键词：
FRAMEWORK; ENERGY;
D O I：
10.1109/MM.2013.28
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
This work proposes an approximate algorithmic transformation and a new class of accelerators, called neural processing units (NPUs). NPUs leverage the approximate algorithmic transformation that converts regions of code from a Von Neumann model to a neural model. NPUs achieve an average 2.3x speedup and 3.0x energy savings for general-purpose approximate programs. This new class of accelerators shows that significant performance and efficiency gains are possible when the abstraction of full accuracy is relaxed in general-purpose computing.
引用
收藏
页码：16 / 27
页数：12
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