A Real-Time End-to-End Event-Based Tactile Sensing System

Event-based tactile sensing offers a promising alternative to frame-based approaches by reducing data redundancy, yet existing systems often lack end-to-end hardware support and remain power-inefficient. This work presents an energy-efficient event-based tactile sensing system that codesigns algorithms and hardware for real-time perception. At the front end, a leakage-compensated event-driven readout circuit integrates multiple tactile sensors into a single node, minimizing wiring and static power. At the algorithm level, a 2-D convolutional neural network (CNN) reconstructs event frames for handwritten digit recognition with strong robustness to varying interaction durations, while a graph neural network (GNN) processes irregular tactile layouts for object classification. A customized event-based tactile processor (ETP) supports end-to-end tactile processing, incorporating a dual-mode event frame builder (EFB) and a neural network processing unit (NPU) with an optimized data-reuse scheme. Evaluated using 28-nm CMOS, the ASIC performs handwritten digit recognition and object classification in 0.44 and 0.37 ms, respectively. The system consumes the total power of 5.2 mW with only 0.11-mW dynamic power, demonstrating an efficient solution for always-on tactile perception.

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