Bringing Touch to the Edge: A Neuromorphic Processing Approach For Event-Based Tactile Systems

Abstract

The rise of neuromorphic applications has highlighted the remarkable potential of biologically-inspired systems. Despite significant advancements in audio and visual technologies, research directed towards tactile sensing has not been as extensive. We propose a neuromorphic tactile system for sensing and processing that presents promising results for edge devices and applications. In this study, a neuromorphic tactile sensor, two data encoding techniques, and a two-layer spiking neural network (SNN) deployed on the AKD1000 Akida Neuromorphic System on Chip (NSoC) were used to demonstrate the system's capabilities. Results from experiments on the ST-MNIST dataset showed high accuracy, with the complement-coded variant achieving 93.1%, outperforming previous state-of-the-art models for this dataset. Additionally, an exploratory study showed that early classification was possible, with most samples requiring only 38% of the available events to classify correctly, reducing the amount of data that needs to be processed. The low power consumption and high throughput of both SNN models, with an average dynamic power consumption of 6.37 mW and 7.76 mW and an average throughput of 586 and 589 frames-per-second respectively, make the proposed system suitable for edge devices with limited power and processing resources. Overall, the proposed tactile sensing system presents a promising solution for edge applications that require high accuracy, low power consumption, and high throughput.