@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 = "Spiking neural networks, Spin Transfer Torque RAM, neuromorphic accelerators, non-volatile memory",
author = "Kulkarni, {Shruti R.} and Shihui Yin and Seo, {Jae Sun} and Bipin Rajendran",
note = "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",
}