SYNtzulA: Open Hardware for Near-Sensor SNN Inference

Spiking Neural Networks (SNNs) exploit event-driven 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-on-chip designed for SNN acceleration, developed using the open-source 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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">mm</i><sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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.