@inproceedings{a33b1978f3684ccd875dc443590835e8,
title = "Neuromorphic Accelerator for Deep Spiking Neural Networks with NVM Crossbar Arrays",
abstract = "In this paper, we present a scalable digital hardware accelerator based on non-volatile memory arrays capable of realizing deep convolutional spiking neural networks (SNNs). Our design studies are conducted using a compact model for spin-transfer torque random access memory (STT-RAM) devices. Large networks are realized by tiling multiple cores which communicate by transmitting spike packets via an on-chip routing network. Compared to an equivalent SRAM based core design, we show that the STT-RAM based design achieves nearly 15X higher GSOPS (Synaptic Operations per Second) per Watt per mm2 making it a promising platform for realizing systems with significant area and power limitations.",
keywords = "neuromorphic accelerators, non-volatile memory, Spiking neural networks, Spin Transfer Torque RAM",
author = "Kulkarni, {Shruti R.} and Shihui Yin and Seo, {Jae Sun} and Bipin Rajendran",
note = "Funding Information: The authors acknowledge the valuable discussions with Anakha V. Babu from NJIT and Shreyas K. Venkataramana-iah from ASU. This research was supported in part by the Semiconductor Research Corporation (2016-SD-2717). Funding Information: Notice: This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a non-exclusive, paid up, irrevocable, world-wide license to publish or reproduce the published form of the manuscript, or allow others to do so, for U.S. Government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Emerging Electronics, ICEE 2022 ; Conference date: 11-12-2022 Through 14-12-2022",
year = "2022",
doi = "10.1109/ICEE56203.2022.10118061",
language = "English (US)",
series = "2022 IEEE International Conference on Emerging Electronics, ICEE 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2022 IEEE International Conference on Emerging Electronics, ICEE 2022",
address = "United States",
}