Implementation of signal processing tasks on neuromorphic hardware

Abstract

Because of power and reliability issues, computer architects are forced to explore new types of architectures, such as heterogeneous systems embedding hardware accelerators. Neuromorphic systems are good candidate accelerators that can perform efficient and robust computing for certain classes of applications. We propose a piking neurons based accelerator, with its hardware and software, that can be easily programmed to execute a wide range of signal processing applications. A library of operators is built to facilitate implementation of various types of applications. Automated placement and routing software tools are used to map these applications onto the hardware. Altogether, this system aims at providing to the user a simple way to implement signal processing tasks on neuromorphic hardware.