SWAM: Workload Automatic Mapper for SNN

Abstract

In order to meet the computing requirements of large-scale spiking neural network (SNN), neuromorphic computing systems usually need to adopt large-scale parallel computing platforms. Therefore, how to quickly deter-mine a reasonable number of computing nodes for the SNN workload (that is, how to properly map the workload to the computing platform) to obtain the best performance, better power consumption and other indicators has become one of the key issues that a neuromorphic computing system needs to solve. Firstly, this paper analyzes the SNN workload characteristics and establishes a calculation model for it. Then for the NEST simulator, this paper further instantiates SNN load model of storage, calculation and communication. Finally, this paper designs and implements a NEST-based workload automatic mapper for SNN (SWAM). SWAM can automatically calculate the mapping result and complete the mapping, avoiding the extremely time-consuming manual trial process of workload mapping. SNN typical applications are run on three different computing platforms, ARM+FPGA, ARM, and PC clusters, and the mapping results of SWAM and LM (Levenberg-Marquardt) algorithm fitting and measured are compared. Experi-mental results show that the average mapping accuracy of SWAM reaches 98.833%. Compared with the LM method and the measured mapping, SWAM has absolute advantage in time cost.

Keywords

• spiking neural network (snn)
• workload mapping
• pynq clusters
• field programmable gate array (fpga) acceleration
• nest simulator