TY - GEN
T1 - Monolithic 3D IC designs for low-power deep neural networks targeting speech recognition
AU - Chang, Kyungwook
AU - Kadetotad, Deepak
AU - Cao, Yu
AU - Seo, Jae-sun
AU - Lim, Sung Kyu
PY - 2017/8/11
Y1 - 2017/8/11
N2 - In recent years, deep learning has become widespread for various real-world recognition tasks. In addition to recognition accuracy, energy efficiency is another grand challenge to enable local intelligence in edge devices. In this paper, we investigate the adoption of monolithic 3D IC (M3D) technology for deep learning hardware design, using speech recognition as a test vehicle. M3D has recently proven to be one of the leading contenders to address the power, performance and area (PPA) scaling challenges in advanced technology nodes. Our study encompasses the influence of key parameters in DNN hardware implementations towards energy efficiency, including DNN architectural choices, underlying workloads, and tier partitioning choices in M3D. Our post-layout M3D designs, together with hardware-efficient sparse algorithms, produce power savings beyond what can be achieved using conventional 2D ICs. Experimental results show that M3D offers 22.3% iso-performance power saving, convincingly demonstrating its entitlement as a solution for DNN ASICs. We further present architectural guidelines for M3D DNNs to maximize the power saving.
AB - In recent years, deep learning has become widespread for various real-world recognition tasks. In addition to recognition accuracy, energy efficiency is another grand challenge to enable local intelligence in edge devices. In this paper, we investigate the adoption of monolithic 3D IC (M3D) technology for deep learning hardware design, using speech recognition as a test vehicle. M3D has recently proven to be one of the leading contenders to address the power, performance and area (PPA) scaling challenges in advanced technology nodes. Our study encompasses the influence of key parameters in DNN hardware implementations towards energy efficiency, including DNN architectural choices, underlying workloads, and tier partitioning choices in M3D. Our post-layout M3D designs, together with hardware-efficient sparse algorithms, produce power savings beyond what can be achieved using conventional 2D ICs. Experimental results show that M3D offers 22.3% iso-performance power saving, convincingly demonstrating its entitlement as a solution for DNN ASICs. We further present architectural guidelines for M3D DNNs to maximize the power saving.
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U2 - 10.1109/ISLPED.2017.8009175
DO - 10.1109/ISLPED.2017.8009175
M3 - Conference contribution
AN - SCOPUS:85028622838
T3 - Proceedings of the International Symposium on Low Power Electronics and Design
BT - ISLPED 2017 - IEEE/ACM International Symposium on Low Power Electronics and Design
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE/ACM International Symposium on Low Power Electronics and Design, ISLPED 2017
Y2 - 24 July 2017 through 26 July 2017
ER -