Comparing Neuromorphic Systems by Solving Sudoku Problems

Abstract

In the field of neuromorphic computing several hardware accelerators for spiking neural networks have been introduced, but few studies actually compare different systems. These comparative studies reveal difficulties in porting an existing network to a specific system and in predicting its performance indicators. Finding a common network architecture that is suited for all target platforms and at the same time yields decent results is a major challenge. In this contribution, we show that a winner-takes-all inspired network structure can be employed to solve Sudoku puzzles on three diverse hardware accelerators. By exploring several network implementations, we measured the number of solved puzzles in a set of 100 assorted Sudokus, as well as time and energy to solution. Concerning the last two indicators, our measurements indicate that it can be beneficial to port a network to an analogue hardware system.