Spike Vision: A Fully Spiking Neural Network Transformer-Inspired Model for Dynamic Vision Sensors

Dynamic Vision Sensors (DVS) offer unique advantages, such as high temporal resolution and low power consumption, making them ideal for low-latency, energy-efficient applications. Current techniques frequently underutilize their capabilities because they depend on conventional frame-based deep neural networks, which sacrifice temporal detail and demand high computational resources. In this work, we propose SpikeVision, a Transformer-inspired Spiking Neural Network (SNN) model with a fully event-based encoding and processing strategy tailored for DVS input streams. SpikeVision integrates attention-inspired mechanisms adapted for spiking computations, enabling efficient spatial feature extraction without relying on matrix multiplications while leveraging stateful neurons for temporal event processing. We demonstrate that SpikeVision achieves state-of-the-art classification accuracy (99.3%) on the DVS128 Gesture benchmark while maintaining low energy consumption in Field-Programmable Gate Array (FPGA) implementations, highlighting its potential for real-time, edge-based vision tasks.