gelneuro-neuromorphic-tactile-system

name	gelneuro-neuromorphic-tactile-system
description	Fully integrated sensing-computing neuromorphic visuo-tactile system for texture recognition on edge hardware
tags	["neuromorphic","tactile-sensing","spiking-neural-network","edge-computing","robotics","texture-recognition","low-power"]
source	arXiv:2607.05241v1
created	2026-07-08T00:00:00.000Z

GelNeuro: Neuromorphic Tactile System for Texture Recognition

Core Innovation

First fully integrated sensing-computing visuo-tactile system that directly pairs GelSight Mini optical tactile sensor with Speck2f neuromorphic SoC, achieving 96.3% accuracy with only 19.6 mW power consumption.

System Architecture

1. Sensing Front-End: GelSight Mini

• Optical tactile sensor with elastomer gel
• Captures contact-induced marker motions
• High-resolution tactile imaging
• Converts physical contact to visual data

2. Neuromorphic Processing: Speck2f SoC

• Dynamic Vision Sensor (DVS) captures marker motions as events
• On-chip spiking convolutional neural network (SCNN) classifier
• Integrated event routing and processing
• 8-bit quantized deployment

3. Hardware-Aware Optimization

• Weight clamping strategy mitigates 8-bit deployment accuracy loss
• Maintains performance under quantization constraints
• Optimized for neuromorphic hardware characteristics

Performance Metrics

Accuracy

• 15-class natural texture recognition: 96.3%
• Inference window: 80 ms
• Hardware-in-the-loop testing: Physical chip validation

Power Efficiency

• Board-level active power: 19.6 mW
• Comparison: 3 orders of magnitude lower than CPU/GPU baselines
• Energy efficiency: Ultra-low power for edge deployment

Generalization

• Robust across unseen contact depths
• Maintains performance under varying pressure conditions
• Adapts to different tactile interaction scenarios

Technical Pipeline

1. Tactile Contact: GelSight Mini captures surface texture
2. Event Generation: DVS converts marker motions to spike events
3. On-Chip Routing: Events routed through neuromorphic network
4. SCNN Classification: Spiking convolutional network processes events
5. Texture Prediction: Output texture class within 80 ms

Key Innovations

Direct Sensor-to-Chip Integration

• Eliminates host computer dependency
• No preprocessing or relaying required
• End-to-end edge processing

Hardware-Aware Training

• Accounts for 8-bit quantization during training
• Weight clamping prevents accuracy degradation
• Optimized for neuromorphic hardware constraints

Ultra-Low Power Operation

• 19.6 mW board-level power
• Suitable for battery-operated robots
• Enables always-on tactile sensing

Applications

• Robotic manipulation: Texture-based object recognition
• Prosthetics: Tactile feedback for artificial limbs
• Quality inspection: Surface texture analysis
• Human-robot interaction: Safe contact detection

Comparison with Baselines

Metric	GelNeuro	CPU Baseline	GPU Baseline
Accuracy	96.3%	~95%	~96%
Power	19.6 mW	~20 W	~50 W
Latency	80 ms	~100 ms	~80 ms
Form factor	Edge SoC	Desktop	Desktop

Implementation Details

Hardware

• Sensor: GelSight Mini optical tactile sensor
• Processor: Speck2f neuromorphic SoC
• Event camera: Dynamic Vision Sensor (DVS)
• Deployment: 8-bit quantized SCNN

Software

• Event-driven processing pipeline
• Hardware-aware weight quantization
• Real-time inference engine

Challenges Addressed

1. Host dependency: Previous systems required external preprocessing
2. Power consumption: Conventional systems consume 1000x more power
3. Latency: End-to-end processing reduces system latency
4. Deployment complexity: Integrated system simplifies deployment

Activation Triggers

neuromorphic-tactile, tactile-sensing, texture-recognition, edge-neuromorphic, spiking-convolutional, low-power-robotics, GelSight, Speck2f, visuo-tactile, hardware-aware-quantization

Related Concepts

• Spiking Neural Networks (SNNs)
• Neuromorphic computing
• Tactile sensing
• Edge AI
• Robotics perception
• Hardware-software co-design