Improving the Energy Efficiency of STT-MRAM based Approximate Cache
Wei Zhao1,a, Wei Tong1,b, Dan Feng1,c, Jingning Liu1,d, Zhangyu Chen1,e, Jie Xu1,f, Bing Wu1,g, Chengning Wang1,h, Bo Liu2
1Wuhan National Laboratory for Optoelectronics, Key Laboratory of Information Storage System, Engineering Research Center of data storage systems and Technology, (school of Computer Science & Technology, Huazhong University of Science & Technology), Ministry of Education of China, Wuhan, China
aweiz@hust.edu.cn
btongwei@hust.edu.cn
cdfeng@hust.edu.cn
djnliu@hust.edu.cn
echenzy@hust.edu.cn
fxujie dsal@hust.edu.cn
gwubin200@hust.edu.cn
hchengningwang@hust.edu.cn
2Hikstor Technology Co., LTD, Hangzhou, China
liubo@hikstor.com
ABSTRACT
Approximate computing applications lead to large energy consumption and performance demand for the memory system. However, traditional SRAM based cache cannot satisfy these demands due to high leakage power and limited density. Spin Transfer Torque Magnetic RAM (STT-MRAM) is a promising candidate of cache due to low leakage power and high density. However, STT-MRAM suffers from high write energy. To leverage the ability of tolerating acceptable quality loss via approximations to data, we propose an STT-MRAM based APProximate cache architecture (APPcache) to write/read approximate data thus largely reducing energy. We find many similar elements (e.g. pixels in images) existing in cache lines while running approximate computing applications. Therefore, APPcache uses several lightweight similarity-based encoding schemes to eliminate the similar elements to reduce the data size thus reducing the write energy of STT-MRAM based cache. Besides, we design a software interface to manually control the output quality. APPcache can significantly eliminate similar elements, thus improving energy efficiency. Experimental results show that our scheme can reduce write energy and improve the image raw data compression ratio by 21.9% and 38.0% compared with the state-of-the-art scheme with 1% error rate, respectively. As for the output quality, the losses of all benchmarks are within 5% with 1% error rate.
Keywords: Approximate Computing, STT-MRAM, Energy.
