Synapse device based neuromorphic system for biomedical applications.

Abstract

Despite holding valuable information, unstructured data pose challenges for efficient recognition due to the difficulties in feature extraction using traditional Von-Neumann architecture systems, which are limited by power and time bottlenecks. Although biological neural signals offer crucial insights, they require more effective recognition solutions due to inherent noise and the vast volumes of data. Inspired by the human brain, neuromorphic systems have emerged as promising alternatives because of their parallelism, low power consumption, and error tolerance. By leveraging deep neural networks (DNNs), these systems can recognize imprecise data through two key processes: learning (feature extraction) and testing (feature matching and recognition). During the learning phase, DNNs extract and store unique features such as weight changes in synapse units. In the testing phase, new data are compared with the stored features for recognition. The parallelization of the neuromorphic system enables the efficient processing of large, imprecise datasets with minimal energy consumption. Nevertheless, the hardware implementation is essential for determining the full potential of DNNs. This paper focuses on synapse devices, which are the core units for hardware DNN implementations, and presents a biomedical application example: a rat neural signal recognition system implemented using a synapse device-based neuromorphic system.