Across the Stack Opportunities for Deep Learning Acceleration
Vijayalakshmi Srinivasan, Bruce Fleischer, Sunil Kumar Shukla, Matthew M. Ziegler, Joel A. Silberman, Jinwook Oh, Jungwook Choi, Silvia Melitta Mueller, Ankur Agrawal, Tina Babinsky, Nianzheng Cao, Chia‐Yu Chen, Pierce Chuang, Thomas Fox, George D. Gristede, Michael Guillorn, Howard Haynie, Michael J. Klaiber, Dongsoo Lee, Shih-Hsien Lo · Proceedings of the International Symposium on Low Power Electronics and Design · 2018
The combination of growth in compute capabilities and availability of large datasets has led to a re-birth of deep learning. Deep Neural Networks (DNNs) have become state-of-the-art in a variety of machine learning tasks spanning domains across vision, speech, and machine translation. Deep Learning (DL) achieves high accuracy in these tasks at the expense of 100s of ExaOps of computation; posing significant challenges to efficient large-scale deployment in both resource-constrained environments and data centers.