SYNtzulA: Open Hardware for Near-Sensor SNN Inference

Spiking Neural Networks (SNNs) exploit eventdriven processing to offer high energy efficiency when deploying
Artificial Intelligence (AI) on wearable edge devices. However, specialized hardware is needed to fully take advantage of this
potential, which, despite recent advances, remains expensive and not widely accessible. To address this, open-source Electronic
Design Automation (EDA) tools and Process Design Kits (PDKs) offer a path to democratize the development of neuromorphic
hardware. In this work, we present SYNtzulA, a system-onchip designed for SNN acceleration, developed using the opensource
IHP-SG13G2 130 nm PDK and the OpenROAD toolchain. The chip integrates a RISC-V softcore and a dedicated SNN
accelerator, occupying approximately 6.8 mm2 including I/O pads. It operates at up to 125 MHz, reaching a throughput of 2
Giga Synaptic Operations per second (GSOP/s) with an energy consumption of 36.5 pJ per synaptic operation. The accelerator
can exploit the sparsity of spike-based computation by skipping unnecessary operations, resulting in total energy consumption in the order of a few hundred nanojoules per inference in different use cases involving biosignal analysis.