neuromorphic-spinnaker-asl

name

neuromorphic-spiNNaker-asl

description

Neuromorphic visual attention framework for sign language recognition on SpiNNaker hardware. Combines event-based vision sensors with spiking neural networks for energy-efficient real-time ASL recognition. Use when: deploying low-power gesture/sign recognition, implementing event-based vision on neuromorphic hardware, building SpiNNaker applications, designing energy-efficient computer vision systems, working with DVS (Dynamic Vision Sensor) data.

Neuromorphic Visual Attention for Sign Language Recognition on SpiNNaker

Overview

This methodology presents a neuromorphic visual attention system for American Sign Language (ASL) recognition deployed on SpiNNaker, a many-core neuromorphic computing platform. Published on arXiv (2605.06005v1, May 2026), it demonstrates how event-based vision combined with spiking neural networks (SNNs) can achieve real-time, energy-efficient gesture recognition.

Key Innovation

Event-Based Vision for ASL

• Replaces frame-based cameras with Dynamic Vision Sensors (DVS) that only report pixel-level brightness changes
• Exploits the temporal sparsity of hand gestures — most pixels remain static between frames
• Reduces data bandwidth by 10-100x compared to conventional RGB/depth video

SpiNNaker Deployment

• Maps SNN directly onto SpiNNaker's ARM core mesh with spike-based communication
• Achieves real-time inference at milliwatt power levels
• Demonstrates feasibility of neuromorphic edge deployment for practical applications

Architecture

Event-Based Preprocessing

1. DVS Event Stream: Raw (x, y, t, polarity) events from neuromorphic camera
2. Temporal Windowing: Accumulate events into fixed-duration time bins (e.g., 10-50ms)
3. Event Frame Construction: Convert sparse events into dense representations:
   • Polarity-separated channels (ON/OFF events)
   • Event count histograms per pixel per window
   • Temporal contrast maps

Visual Attention Mechanism

• Spatial Attention: Dynamically weights regions of interest (hands, face) over background
• Temporal Attention: Emphasizes motion onset and transition frames in gesture sequences
• Implemented as SNN: Attention weights computed through spiking neuron dynamics, not softmax

Spiking Neural Network Pipeline

1. Input Encoding: Event frames → spike trains via rate coding or temporal coding
2. Feature Extraction: Convolutional SNN layers for spatial feature learning
3. Temporal Integration: Recurrent SNN (LSNN or similar) for sequence modeling
4. Classification: Readout layer producing ASL letter/word predictions

SpiNNaker-Specific Considerations

Mapping Strategy

• Core Allocation: Assign neuron populations to ARM cores based on connectivity density
• Routing: Use SpiNNaker's packet-switched network for inter-core spike communication
• Memory Constraints: Each core has limited SRAM (~128KB) — requires weight quantization/pruning

Optimization Techniques

• Weight Compression: Use fixed-point arithmetic to reduce memory footprint
• Event Sparsity: Exploit sparse firing to minimize inter-core communication
• Batch Processing: Process multiple time windows in parallel across available cores

Performance Metrics

• Latency: End-to-end inference time (typically < 100ms for real-time)
• Power: Sub-watt operation (vs. 10-100W for GPU-based alternatives)
• Accuracy: Competitive with frame-based deep learning baselines on ASL datasets

Application Workflow

1. Data Collection: Record ASL gestures with DVS camera (or convert existing video to events using DVS simulators)
2. Event Preprocessing: Convert event streams to tensor representations
3. SNN Training: Train on conventional hardware (GPU) using surrogate gradient methods
4. Conversion: Convert trained ANN to SNN or train SNN directly
5. SpiNNaker Mapping: Deploy trained weights onto SpiNNaker hardware
6. Deployment: Real-time inference with event-based input streaming

Key Advantages

• Energy Efficiency: Orders of magnitude lower power than GPU/CPU alternatives
• Low Latency: Event-based processing eliminates frame-rate bottlenecks
• Privacy: Event data contains minimal identity information compared to RGB video
• Robustness: Less sensitive to lighting conditions than frame-based cameras

When to Use

• Edge deployment of gesture/sign recognition in power-constrained environments
• Real-time human-computer interaction systems requiring low latency
• Applications where privacy is critical (event data is harder to reconstruct faces)
• Research into neuromorphic computing for practical computer vision tasks
• Assistive technology for deaf/hard-of-hearing communication

Pitfalls

• DVS sensors are expensive and less widely available than conventional cameras
• Converting existing RGB datasets to event domain introduces simulation-reality gap
• SpiNNaker hardware access may be limited; consider Loihi or other neuromorphic alternatives
• SNN training with surrogate gradients is less stable than standard backpropagation
• Event frame representation choices (window size, encoding scheme) significantly impact performance
• SpiNNaker's ARM-based architecture is different from pure digital neuromorphic chips (Loihi, TrueNorth)

References

• arXiv: 2605.06005v1 — "Neuromorphic visual attention for Sign-language recognition on SpiNNaker"
• SpiNNaker documentation: https://spinnakermanchester.github.io/
• DVS camera: Prophesee, iniVation, or event-based simulation tools