Paper ID: 2411.19611

Memristive Nanowire Network for Energy Efficient Audio Classification: Pre-Processing-Free Reservoir Computing with Reduced Latency

Akshaya Rajesh (1), Pavithra Ananthasubramanian (1), Nagarajan Raghavan (1), Ankush Kumar (1 and 2) ((1) nano-Macro Reliability Laboratory (nMRL), Engineering and Product Development Pillar, Singapore University of Technology and Design, 8, Somapah Road, 487372, Singapore, (2) Centre for Nanotechnology, Indian Institute of Technology Roorkee, Roorkee, Uttrakhand, 247667, India)

Speech recognition is a key challenge in natural language processing, requiring low latency, efficient computation, and strong generalization for real-time applications. While software-based artificial neural networks (ANNs) excel at this task, they are computationally intensive and depend heavily on data pre-processing. Neuromorphic computing, with its low-latency and energy-efficient advantages, holds promise for audio classification. Memristive nanowire networks, combined with pre-processing techniques like Mel-Frequency Cepstrum Coefficient extraction, have been widely used for associative learning, but such pre-processing can be power-intensive, undermining latency benefits. This study pioneers the use of memristive and spatio-temporal properties of nanowire networks for audio signal classification without pre-processing. A nanowire network simulation is paired with three linear classifiers for 10-class MNIST audio classification and binary speaker generalization tests. The hybrid system achieves significant benefits: excellent data compression with only 3% of nanowire output utilized, a 10-fold reduction in computational latency, and up to 28.5% improved classification accuracy (using a logistic regression classifier). Precision and recall improve by 10% and 17% for multispeaker datasets, and by 24% and 17% for individual speaker datasets, compared to raw data classifiers.This work provides a foundational proof of concept for utilizing memristive nanowire networks (NWN) in edge-computing devices, showcasing their potential for efficient, real-time audio signal processing with reduced computational overhead and power consumption, and enabling the development of advanced neuromorphic computing solutions.

Submitted: Nov 29, 2024