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.

翻译：脉冲神经网络（SNN）的兴起及其应用正推动神经形态架构快速发展，这类架构能模拟生物神经元典型的脉冲式数据处理方式。SNN具有高效功耗和并行计算能力，促使研究人员开发数字加速器，利用这些特性在边缘设备上实现快速低功耗计算。然而，数字神经形态硬件的普及受到芯片流片高昂成本的制约，因此以现场可编程门阵列（FPGA）为目标平台可成为可行替代方案——它提供灵活经济的数字神经形态系统实现途径，并助力开源硬件设计的推广。本文提出一种异构系统级芯片（SoC），通过传统处理器集成管理递归SNN加速器ReckOn的运行，这些处理器包括基于RISC-V的开源微控制器X-HEEP和Zynq Ultrascale系统中的ARM处理器。我们通过FPGA实现ReckOn的流片版本来验证设计，复现分类结果以确保精度等价性及物理实现特性的一致性。在第二组实验中，我们评估该方案在分类Braille数字数据集子集时的在线学习能力——该数据集近期被用于比较神经形态框架与平台。