Paper Info
Title
HR<sup>3</sup>AM: A Heat Resilient Design for RRAM-based Neuromorphic Computing
Abstract
RRAM based accelerators have been widely adopted in many neuromorphic designs. However, RRAM cells are sensitive to temperature, which changes RRAM’s conductance. Such heat-induced interference can significantly decrease the computational accuracy because values are functions of RRAM conductance. In this paper, we propose HR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> AM, a heat resilience design, which improves accuracy and optimizes the thermal distribution of RRAM based neural network accelerators. HR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> AM consists of two key mechanisms: bitwidth downgrading and tile pairing. Bitwidth downgrading re-represents weights by shifting the conductance to improve the network inference accuracy. Tile pairing matches hot crossbar units with pre-defined idle units to mitigate high-temperature issues. We evaluated HR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> AM on four real world neural network models. Results show that HR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> AM improves classification accuracy by up to 41.8% compared with current state-of-the-art designs. For thermal optimization, HR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> AM effectively decreases the maximum temperature by 6.2K and average temperature by 6K.
Year
DOI
Venue
2019
10.1109/ISLPED.2019.8824926
2019 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)
Keywords
DocType
ISBN
hot crossbar units,real world neural network models,bitwidth downgrading,HR3AM,thermal optimization,high-temperature issues,pre-defined idle units,network inference accuracy,tile pairing,RRAM based neural network accelerators,heat resilience design,RRAM conductance,heat-induced interference,RRAM cells,neuromorphic designs,RRAM-based neuromorphic computing,heat resilient design
Conference
978-1-7281-2955-6
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
ying liu136446.92
Mingxuan Zhou200.34
Tajana Simunic33198266.23
Jishen Zhao463838.51