Spiking Neural Networks(SNN) have been widely leveraged by neuromorphic systems due to their ability to closely mimic biological neural behavior, where information is exchanged and received between neurons in the form of sparse events(spikes). Such neuromorphic systems are highly energy-efficient because the use of a global clock can be avoided by asynchronous event-driven operations. Neurons, as the basic processing units of neuromorphic systems, are required to be low-power and high-speed for the implementation of complex networks. In this work, two fully event-driven digital Integrate-and-Fire(IF) neuron design is presented. Both design exploits the hierarchical structure, which allows the synaptic weights can be accumulated by local compute units in parallel. Instead of using handshake protocols, the proposed design generates on-demand event pulses to drive the weight accumulation, so we call it self-timed. Both neurons are designed by SystemVerilog and synthesized in TSMC 28nm technology. According to the synthesis results, both designs can finish the accumulation of 1024 6-bit weights within 100ns, with a power consumption of 0.055pJ per spike and 0.23pJ per spike respectively.

A Spiking neural network (SNN) is a type of artificial neural network which encodes information using spike timing, network structure, and synaptic weights to emulate the information processing function of the human brain. Within an SNN, it is always required to support the spike transmission that travels between neurons(array). This thesis aims to design a customized high-speed interconnect system which supports multi-point communication in a neuromorphic computing system. The burst-mode two-wire protocol in point-to-point communication is applied in this interconnect system, which is designed in high-level modelling with SystemC. In order to improve the utilization of hardware resources, a virtual channel system is involved. Furthermore, this system could be extended to a variable number of neuron arrays to support different types of spiking neural networks. Also, optimization methods are adopted to increase the transmission rate of the system and save unnecessary energy consumption. The interconnect system could achieve a throughput of 3.802 Gbits/s with the given MNIST use case, based on the evaluation of simulation results.