Authors:
Jashkumar Shah, Aashna Desai, Rugved Gramopadhye, Tina Nenshi Gada, Debabrata Das, S. Suman Rajest
Addresses:
Department of Information Technology, Illinois Institute of Technology, Chicago, Illinois, United States of America. Department of Information Technology, Pace University, New York, United States of America. Department of Information Technology, The University of Texas at Dallas, Texas, United States of America. Department of Human Computer Interaction, State University of New York at Oswego, New York, United States of America. Department of Information Technology, The University of Texas at Austin, Texas, United States of America. Department of Research and Development, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.
Abstract:
Brain-inspired neuromorphic computing, which is based on the neuronal structure of the brain, provides a revolutionary paradigm for real-time edge computing that is efficient in terms of energy consumption. The purpose of this work is to investigate the role of synaptic plasticity, specifically spike-timing-dependent plasticity (STDP), in increasing computational efficiency in neuromorphic edge device topologies. A new architecture is shown here that uses STDP to facilitate on-chip learning and adaptation to a wide range of sensory inputs. Additionally, this design features reduced cloud processing, lower latency, and improved energy efficiency. The classification of streams online is the primary focus of our efforts. This is a fundamental operation performed in most edge operations, including autonomous navigation and health monitoring on wearable devices. Our model is trained and evaluated in a Python-based simulation environment, and Brian2 is used to simulate neuronal dynamics. The performance of the novel architecture is evaluated using the Spiking Heidelberg Digits (SHD) benchmark, an appropriate metric for spike-based classification of auditory samples. This architecture demonstrates higher processing speed and energy efficiency than traditional von Neumann architectures, achieving 96.4% classification accuracy and a mean power consumption of 2.7 milliwatts.
Keywords: Neuromorphic Computing; Synaptic Plasticity; Edge Processing; Spiking Heidelberg Digits (SHD); Real-Time Systems; Internet of Things (IoT); Health Monitoring; Energy Consumption.
Received: 04/10/2024, Revised: 19/11/2024, Accepted: 03/02/2025, Published: 07/12/2025
DOI: 10.64091/ATICL.2025.000232
AVE Trends in Intelligent Computer Letters, 2025 Vol. 1 No. 4 , Pages: 179-187