OpenSpike: An OpenRAM SNN Accelerator
Farhad Modaresi a, Matthew Guthaus b, Jason Eshraghian b
a Allameh Mohaddes Nouri University
b University of California, Santa Cruz, 1156 High Street, Santa Cruz, United States
Proceedings of Neuromorphic Materials, Devices, Circuits and Systems (NeuMatDeCaS)
VALÈNCIA, Spain, 2023 January 23rd - 25th
Organizers: Rohit Abraham John, Irem Boybat, Jason Eshraghian and Simone Fabiano
Contributed talk, Farhad Modaresi, presentation 027
DOI: https://doi.org/10.29363/nanoge.neumatdecas.2023.027
Publication date: 9th January 2023

This presentation will present a spiking neural network 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 mm2. The throughput of the accelerator is 48,262 images per second with a wallclock time of 20.72 µs. 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.

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