Minimally buffered deflection router for spiking neural network hardware implementations

Spiking neural networks (SNNs) have the potential to closely mimic the information processing of biological brains, by using massive neurons that are interconnected in a complex network. Recent researches have considered using electronic hardware circuits to SNN implementations to meet real-time processing requirements. Network-on-Chips (NoCs) have been widely used to develop such SNN circuits as their interconnections can offer stable interconnectivity for neuron communications with high throughput and real-time execution. However, its scalability is limited due to expensive and complex NoC routers which leads to high energy consumption and large area utilization. Therefore, a minimally buffered deflection router (MBDR) is proposed in this work to address the scalability challenge of the hardware SNNs. It employs a deflection router technique to remove most of the inter-buffers and other expensive components of the conventional routers. Moreover, a novel flow controller is developed in MBDR to further reduce power consumption. Compared to existing approaches, experimental results show that based on 90-nm CMOS technology the area and power consumption of the proposed router are reduced by similar to 86% and similar to 88%, respectively. In the meantime, system throughput is maintained at a high level.

Automated summary

This study introduces a minimally buffered deflection router (MBDR) to address the scalability challenge of hardware SNNs, which reduces energy consumption and costs while maintaining a high level of system throughput through the use of deflection router technique and a novel flow controller.