Abstract
In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) emphasizing on integrating dual information streams within the neocortical pyramidal neurons specifically Context- Dependent Leaky Integrate and Fire (CLIF) neuron models, essential element in RSNN. We present a quantized version of the CLIF neuron (qCLIF), developed through a hardware-software codesign approach utilizing the sparse activity of RSNN. Implemented in a 45nm technology node, the qCLIF is compact (900um^2) and achieves a high accuracy of 90% despite 8 bit quantization on DVS gesture classification dataset. Our analysis spans a network configuration from 10 to 200 qCLIF neurons, supporting up to 82k synapses within a 1.86 mm^2 footprint, demonstrating scalability and efficiency
Abstract (translated)
在这项研究中，我们提出了一个基于上下文信息的循环神经网络（RSNN）的硬件实现，重点关注将双信息流集成到轴突颗粒细胞特别是Context- Dependent Leaky Integrate和Fire（CLIF）神经元模型中，这是RSNN的必要组成部分。我们呈现了通过使用RSNN的稀疏活动开发的量化CLIF神经元（qCLIF）。在45nm技术节点上实现，qCLIF是紧凑的（900um2）且具有90%的高准确度，尽管在DVS手势分类数据集上进行了8位量化。我们的分析跨越了网络配置从10到200个qCLIF神经元，支持在1.86mm2的足迹内达到82k个突触。这证明了可扩展性和效率。
URL
https://arxiv.org/abs/2404.18066
