The brain-inspired spiking neural network （SNN） computing paradigm offers the potential for low-power and scalable computing, suited to many intelligent tasks that conventional computational systems find difficult. ...The brain-inspired spiking neural network （SNN） computing paradigm offers the potential for low-power and scalable computing, suited to many intelligent tasks that conventional computational systems find difficult. On the other hand, NoC （network-on-chips） based very large scale integration （VLSI） systems have been widely used to mimic neuro- biological architectures （including SNNs）. This paper proposes an evaluation methodology for SNN applications from the aspect of micro-architecture. First, we extract accurate SNN models from existing simulators of neural systems, second, a cycle-accurate NoC simulator is implemented to execute the aforementioned SNN applications to get timing and energy-consumption information. We believe this method not only benefits the exploration of NoC design space but also bridges the gap between applications （especially those from the neuroscientists＇ community） and neuromorphic hardware. Based on the method, we have evaluated some typical SNNs in terms of timing and energy. The method is valuable for the development of neuromorphic hardware and applications.展开更多
文摘The brain-inspired spiking neural network （SNN） computing paradigm offers the potential for low-power and scalable computing, suited to many intelligent tasks that conventional computational systems find difficult. On the other hand, NoC （network-on-chips） based very large scale integration （VLSI） systems have been widely used to mimic neuro- biological architectures （including SNNs）. This paper proposes an evaluation methodology for SNN applications from the aspect of micro-architecture. First, we extract accurate SNN models from existing simulators of neural systems, second, a cycle-accurate NoC simulator is implemented to execute the aforementioned SNN applications to get timing and energy-consumption information. We believe this method not only benefits the exploration of NoC design space but also bridges the gap between applications （especially those from the neuroscientists＇ community） and neuromorphic hardware. Based on the method, we have evaluated some typical SNNs in terms of timing and energy. The method is valuable for the development of neuromorphic hardware and applications.
