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OpenSpike
OpenSpike a spiking neural network (SNN) accelerator made using fully open-source EDA tools, process design kit (PDK), and memory macros synthesized using OpenRAM. The chip is taped out in the 130 nm SkyWater process and integrates over 1 million synaptic weights, and offers a reprogrammable architecture. It operates at a clock speed of 40 MHz, a supply of 1.8 V, uses a PicoRV32 core for control, and occupies an area of 33.3 mm<sup>2</sup>. The throughput of the accelerator is 48,262 images per second with a wallclock time of 20.72 μs, at 56.8 GOPS/W. The spiking neurons use hysteresis to provide an adaptive threshold (i.e., a Schmitt trigger) which can reduce state instability. This results in high performing SNNs across a range of benchmarks that remain competitive with state-of-the- art, full precision SNNs.
<p align="center" float="center"> <img src="docs/intro.png"/> </p>Citation
OpenSpike has been accepted for presentation at the 2023 IEEE Symposium on Circuits and Systems in Monterey, CA, USA. The preprint is available here.
If you find OpenSpike useful in your work, please cite the following source:
@inproceedings{modaresi2023openspike,
title = {OpenSpike: An OpenRAM SNN Accelerator}
author = {Modaresi, Farhad and Guthaus, Matthew and Eshraghian, Jason K},
journal = {arXiv preprint arXiv:2302.01015},
year = {2023}
}