Extensible neuromorphic computing simulator based on a programmable hardware

The CPU based software simulator for neuromorphic computing is faced with problems of high power consumption and speed limitation of interconnection network, especially in large-scale Spiking Neural Network (SNN) simulation. IBM, Stanford, and ARM have demonstrated us their solutions for neuromorphic computing. However, the cost and development cycle make these approaches impractical for general research. In this paper, we provide an extensible and programmable hardware platform which is suitable for large-scale neuromorphic computing and simulation. The design of hardware simulator is based on Altera Stratix V FPGA, and its most significant advantage consists in parallel processing capability. With NIOS II soft core processor and Gigabit Ethernet interface, the simulator is programmable for different applications and is very adaptable for neural network configuration. Two kinds of routing topologies are available for network performance simulation. By inserting statistic modules into the computing core, it´s possible to monitor the single core state as well as the network state, which is of great importance in routing strategy establishing. Furthermore, this extensible simulator supports at least 10⁵-neuron-network computing.