AC-DSE: Approximate Computing for the Design Space Exploration of Reconfigurable MPSoCs

Abstract

This paper concerns the design space exploration (DSE) of Reconfigurable Multi- Processor System-on- Chip (MPSoC) architectures. Reconfiguration allows users to allocate optimum system resources for a specific application in such a way to improve the energy and throughput balance. To achieve the best balance between power consumption and throughput performance for a particular application domain, typical design space parameters for a multi-processor architecture comprise the cache size, the number of processor cores and the operating frequency. The exploration of the design space has always been an offline technique, consuming a large amount of time. Hence, the exploration has been unsuitable for reconfigurable architectures, which require an early runtime decision. This paper presents Approximate Computing DSE (AC-DSE), an online technique for the DSE of MPSoCs by means of approximate computing. In AC-DSE, design space solutions are first obtained from a set of optimization algorithms, which in turn are used to train a neural network (NN). From then on, the NN can be used to rapidly return its own solutions in the form of design space parameters for a desired energy and throughput performance, without any further training.