標題: Sensor-Based Approximate Adder Design for Accelerating Error-Tolerant and Deep-Learning Applications
作者: Huang, Ning-Chi
Chen, Szu-Ying
Wu, Kai-Chiang
資訊工程學系
Department of Computer Science
公開日期: 1-一月-2019
摘要: Approximate computing is an emerging strategy which trades computational accuracy for computational cost in terms of performance, energy, and/or area. In this paper, we propose a novel sensor-based approximate adder for high-performance energy-efficient arithmetic computation, while considering the accuracy requirement of error-tolerant applications. This is the first work using in-situ sensors for approximate adder design, based on monitoring online transition activity on the carry chain and speculating on carry propagation/truncation. On top of a fully-optimized ripple-carry adder, the performance of our adder is enhanced by 2.17X. When applied in error-tolerant applications such as image processing and handwritten digit recognition, our approximate adder leads to very promising quality of results compared to the case when an accurate adder is used.
URI: http://hdl.handle.net/11536/152478
ISBN: 978-3-9819263-2-3
ISSN: 1530-1591
期刊: 2019 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)
起始頁: 692
結束頁: 697
顯示於類別:會議論文