首个嵌入式FPGA加速器实现脑启发神经网络的在线学习与高效推理。
Embedded FPGA Acceleration of Brain-Like Neural Networks: Online Learning to Scalable Inference
- 基于高层次综合在Zynq SoC上构建BCPNN加速器，支持在线学习和混合精度。
- 在MNIST等数据集上比ARM基线快17.5倍，能耗降低94%且精度不变。
- 适合低功耗边缘设备上的自适应智能应用，推动类脑计算落地。
边缘AI应用日益需要能在本地学习和自适应、且能耗极低的模型。传统深度学习模型虽强大，但参数过多、功耗高，依赖云端。脑启发神经网络（BLNNs）如贝叶斯置信传播神经网络（BCPNN）通过模拟皮层结构和生物约束学习规则，提供稀疏架构与局部学习机制，支持无监督/半监督学习，更适合低功耗边缘智能。然而，现有BCPNN实现依赖GPU或数据中心FPGA，难以部署于嵌入式系统。本文首次在Zynq UltraScale+ SoC上使用高层次综合实现了BCPNN的嵌入式FPGA加速器，支持在线学习与仅推理内核，并具备可变精度与混合精度能力。在MNIST、肺炎和乳腺癌数据集上评估，相比ARM基线最高实现17.5倍延迟降低和94%能耗节省，且不损失精度。该工作使类脑计算在边缘设备上成为可能，弥合了脑启发学习与实际部署之间的差距。
原文摘要 · Abstract (English)
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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