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    Author(s)
    Display Name
    Farhad Modaresi
    Affiliation
    Affiliation
    Allameh Mohaddes Nouri University
    Display Name
    Matthew Guthaus
    Affiliation
    Affiliation
    University of California, Santa Cruz
    Display Name
    Jason Eshraghian
    Affiliation
    Affiliation
    UC Santa Cruz
    Abstract

    This paper presents a spiking neural network accelerator 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 us. This results in high performing SNNs across a range of benchmarks that remain competitive with state-of-the-art, full precision SNNs.