A hardware architecture for accelerating neuromorphic vision algorithms

Ahmed Al-Maashri, Michael DeBole, Chi-Li Yu, Vijaykrishnan Narayanan, Chaitali Chakrabarti

Abstract: Neuromorphic vision algorithms are biologically inspired algorithms that follow the processing that takes place in the visual cortex. These algorithms have proved to match classical computer vision algorithms in classification performance and even outperformed them in some instances. However, neuromorphic algorithms suffer from high complexity leading to poor execution times when running on general purpose processors, making them less attractive for real-time applications. FPGAs, on the other hand, have become true signal processing platforms due to their lightweight, low power consumption and massive parallel computational resources. This paper describes an FPGA-based hardware architecture that accelerates an object classification cortical model, HMAX. Compared to a CPU implementation, this hardware accelerator offers 23X (89X) speedup when mapped to a single-FPGA (multi-FPGA) platform, while maintaining a classification accuracy of 92.5%.