YANA: Bridging the Neuromorphic Simulation-to-Hardware Gap

TL;DR

YANA is an FPGA-based neuromorphic accelerator that enables efficient, scalable SNN processing, bridging the gap between simulation and hardware for real-time, power-constrained applications.

Contribution

It introduces YANA, a flexible, open-source FPGA framework supporting arbitrary SNN topologies with efficient event-driven processing and sparsity exploitation.

Findings

YANA achieves near-linear scaling with sparsity levels in inference.

Supports up to 2^17 synapses and 2^10 neurons on AMD Kria platform.

Open-source framework integrates with neuromorphic tools via NIR.

Abstract

Spiking Neural Networks (SNNs) promise significant advantages over conventional Artificial Neural Networks (ANNs) for applications requiring real-time processing of temporally sparse data streams under strict power constraints -- a concept known as the Neuromorphic Advantage. However, the limited availability of neuromorphic hardware creates a substantial simulation-to-hardware gap that impedes algorithmic innovation, hardware-software co-design, and the development of mature open-source ecosystems. To address this challenge, we introduce Yet Another Neuromorphic Accelerator (YANA), an FPGA-based digital SNN accelerator designed to bridge this gap by providing an accessible hardware and software framework for neuromorphic computing. YANA implements a five-stage, event-driven processing pipeline that fully exploits temporal and spatial sparsity while supporting arbitrary SNN topologies through point-to-point neuron connections. The architecture features an input preprocessing scheme that maintains steady event processing at one event per cycle without buffer overflow risks, and implements hardware-efficient event-driven neuron updates using lookup tables for leak calculations. We demonstrate YANA's sparsity exploitation capabilities through experiments on the Spiking Heidelberg Digits dataset, showing near-linear scaling of inference time with both spatial and temporal sparsity levels. Deployed on the accessible AMD Kria KR260 platform, a single YANA core utilizes 740 LUTs, 918 registers, 7 BRAMS and 24 URAMs, supporting up to $2^{17}$ synapses and $2^{10}$ neurons. We release the YANA framework as an open-source project, providing an end-to-end solution for training, optimizing, and deploying SNNs that integrates with existing neuromorphic computing tools through the Neuromorphic Intermediate Representation (NIR).