Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference

• URL: http://arxiv.org/abs/2506.18530v1
• Date: Mon, 23 Jun 2025 11:35:20 GMT
• Title: Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference
• Authors: Muhammad Ihsan Al Hafiz, Naresh Ravichandran, Anders Lansner, Pawel Herman, Artur Podobas,
• Abstract summary: We present the first embedded FPGA accelerator for BCPNN on a Zynq UltraScale+ system using High-Level Synthesis.<n>Our accelerator achieves up to 17.5x latency and 94% energy savings over ARM baselines, without sacrificing accuracy.<n>This work enables practical neuromorphic computing on edge devices, bridging the gap between brain-like learning and real-world deployment.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Edge AI applications increasingly require models that can learn and adapt on-device with minimal energy budget. Traditional deep learning models, while powerful, are often overparameterized, energy-hungry, and dependent on cloud connectivity. Brain-Like Neural Networks (BLNNs), such as the Bayesian Confidence Propagation Neural Network (BCPNN), propose a neuromorphic alternative by mimicking cortical architecture and biologically-constrained learning. They offer sparse architectures with local learning rules and unsupervised/semi-supervised learning, making them well-suited for low-power edge intelligence. However, existing BCPNN implementations rely on GPUs or datacenter FPGAs, limiting their applicability to embedded systems. This work presents the first embedded FPGA accelerator for BCPNN on a Zynq UltraScale+ SoC using High-Level Synthesis. We implement both online learning and inference-only kernels with support for variable and mixed precision. Evaluated on MNIST, Pneumonia, and Breast Cancer datasets, our accelerator achieves up to 17.5x latency and 94% energy savings over ARM baselines, without sacrificing accuracy. This work enables practical neuromorphic computing on edge devices, bridging the gap between brain-like learning and real-world deployment.

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