PEST: Energy-Efficient NEST Brain-Like Simulator Implemented by PYNQ Cluster

Abstract

Large-scale brain-like simulation with high performance and low power consumption is one of the most challenging problems in brain-like computing. At present, the implementation of brain-like computing is mainly divided into hardware implementation and software implementation. Dedicated brain-like computing chips and systems implemented by hardware can provide better energy efficiency indicators, but they are costly and poorly adaptable. Software-based simulation (such as NEST) has good availability but has the problem of slow computing speed. If the two implementation methods are combined, through the software and hardware co-design, to ensure a good application ecology while obtaining higher computing energy efficiency, this paper proposes a high-energy-efficiency implemen-tation (PEST) of the NEST brain-like simulator based on the FPGA heterogeneous platform PYNQ cluster. By building a large-scale PYNQ cluster, it designs software and hardware data interaction interfaces to realize a scalable brain-like computing system based on the NEST simulator, designs FPGA hardware circuits for IAF neurons, and uses MPI distributed computing to improve NEST computing efficiency. The experimental results show that, for different computing models, under the optimal adaptation of PYNQ cluster, the performance of the neuron update part on PEST is improved by more than 4.6 times compared with AMD 3600X, and by more than 7.5 times compared with Xeon 2620. PEST's updated energy efficiency is more than 5.3 times higher than that of 3600X and 7.9 times higher than that of Xeon 2620.

Keywords

• brain-like computing
• spiking neural networks
• nest simulator
• field programmable gate array (fpga)
• pynq framework