This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA). ODESA is the first network to have end-to-end multi-layer online local supervised training without using gradients and has the combined adaptation of weights and thresholds in an efficient hierarchical structure. This research shows that the network architecture and the online training of weights and thresholds can be implemented efficiently on a large scale in hardware. The implementation consists of a multi-layer Spiking Neural Network (SNN) and individual training modules for each layer that enable online self-learning without using back-propagation. By using simple local adaptive selection thresholds, a Winner-Takes-All (WTA) constraint on each layer, and a modified weight update rule that is more amenable to hardware, the trainer module allocates neuronal resources optimally at each layer without having to pass high-precision error measurements across layers. All elements in the system, including the training module, interact using event-based binary spikes. The hardware-optimized implementation is shown to preserve the performance of the original algorithm across multiple spatial-temporal classification problems with significantly reduced hardware requirements.

翻译：本文提出了最近提出的优化深度事件驱动脉冲神经网络架构(ODESA)的高效硬件实现方法。ODESA是首个无需梯度即可实现端到端多层在线局部监督训练的网络，且具备权重与阈值在高效层次结构中的联合自适应能力。研究表明，该网络架构及其权重与阈值的在线训练可在大规模硬件中高效实现。该实现包含多层脉冲神经网络(SNN)以及每层独立的训练模块，无需反向传播即可实现在线自学习。通过采用简单的局部自适应选择阈值、每层"胜者全得"(WTA)约束以及更适配硬件的改进权重更新规则，训练模块能在无需跨层传递高精度误差测量的情况下，在每一层优化分配神经资源。系统中所有元素（包括训练模块）均基于事件驱动的二进制脉冲进行交互。经硬件优化的实现在多个时空分类问题上保留了原始算法的性能，同时显著降低了硬件资源需求。