Yang, Shuangming, Wang, Haowen, and Rahimi Azghadi, Mostafa (2023) Evolutionary Optimization of Neuromorphic Architecture for Low-power Cerebellum Prosthetic Instrumentation and Device in Biomedical Systems. In: Proceedings of the IEEE International Instrumentation and Measurement Technology Conference. From: I2MTC 2023: IEEE International Instrumentation and Measurement Technology Conference, 22-25 May 2023, Kuala Lumpur, Malaysia.

PDF (Published Version) - Published Version Restricted to Repository staff only

 

1

1

Abstract

Neuromorphic computing is a new generation of technique, which has been used in the neuroprosthetic implementation in biomedical applications. The spike-based computing mechanism enables it with low power consumption and real-time speed. However, there is a lack of optimization strategy for neuromorphic architecture of neuroprosthetic. In this paper, a novel optimization strategy for neuromorphic architecture of neuroprosthetic, named the Evolutionary Neuromorphic Optimization Framework (ENOF), is presented. A HEMA algorithm is proposed for the implementation of ENOF. It can continuously find the optimal mapping scheme and achieve better accuracy by jumping out of local optimization. Experimental results show that the proposed method can cut down the energy consumption and have better stability. Better optimization can be achieved along with the NoC scale increasing. The proposed work is meaningful for the low-power prosthetic instrumentation and device of biomedical systems, and can be applied in healthcare or clinical situations.

Actions (Repository Staff Only)

Item Control Page