在FPGA上构建异构芯片，实现开源脉冲神经网络加速器的高效边缘计算。
Heterogeneous SoC Integrating an Open-Source Recurrent SNN Accelerator for Neuromorphic Edge Computing on FPGA
- 将开源递归脉冲神经网络加速器ReckOn与RISC-V和ARM处理器集成于异构SoC中。
- 在FPGA上复现ReckOn设计，准确率与硅片版本一致，物理特性匹配。
- 支持在线学习，可在布雷尔数字数据集上实时分类，适合边缘智能场景。
脉冲神经网络（SNNs）因其类生物神经元的脉冲式数据处理机制，正推动类脑计算架构的快速发展。其低功耗与并行计算优势促使研究者开发数字加速器，以在边缘设备上实现快速、低功耗计算。然而，数字类脑硬件的推广受限于硅片流片成本高昂，因此采用现场可编程门阵列（FPGA）作为灵活且低成本的替代方案，有助于推动开源硬件设计的普及。本文提出一种异构系统级芯片（SoC），通过集成RISC-V开源微控制器X-HEEP与Zynq Ultrascale中的ARM处理器，管理开源递归脉冲神经网络加速器ReckOn的运算。我们通过在FPGA上实现已流片的ReckOn版本，验证了其分类结果的准确性与物理实现特性的等价性。此外，进一步实验评估了该方案在最新用于对比类脑框架的布雷尔数字数据集子集上的在线学习能力。
原文摘要 · Abstract (English)
The growing popularity of Spiking Neural Networks (SNNs) and their applications has led to a significant fast-paced increase of neuromorphic architectures capable of mimicking the spike-based data processing typical of biological neurons. The efficient power consumption and parallel computing capabilities of the SNNs lead researchers towards the development of digital accelerators, which exploit such features to bring fast and low-power computation on edge devices. The spread of digital neuromorphic hardware however is slowed down by the prohibitive costs that the silicon tape out of circuits brings, that's why targeting Field Programmable Gate Arrays (FPGAs) could represent a viable alternative, offering a flexible and cost-effective platform for implementing digital neuromorphic systems and helping the spread of open-source hardware designs. In this work we present an heterogeneous System-on-Chip (SoC) where the operations of ReckOn, a Recurrent SNN accelerator, are managed through the integration with traditional processors. These include the RISC-V-based, open-source microcontroller X-HEEP and the ARM processor featured in Zynq Ultrascale systems. We validate our design by reproducing the classification results through the implementation on FPGA of the taped-out version of ReckOn in order to check the equivalence of the accuracy and the characteristics in terms of physical implementation. In a second set of experiments, we evaluate the online learning capability of the solution in classifying a subset of the Braille digit dataset recently used to compare neuromorphic frameworks and platforms.
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