SNNF: An SNN-based Near-Sensor Noise Filter for Dynamic Vision Sensors

TL;DR

This paper introduces SNNF, a near-sensor noise filtering method for DVS that uses a compact binary image, a single-layer SNN classifier, and achieves high efficiency and accuracy on FPGA and ASIC hardware.

Contribution

The paper presents a novel, hardware-efficient SNN-based noise filter for DVS that reduces resource usage while maintaining high filtering accuracy.

Findings

SNNF achieves an AUC of 0.89 on standard datasets.

FPGA implementation reduces memory and logic resources to 11% and 40%.

ASIC implementation achieves 44.4 Meps with minimal area and power consumption.

Abstract

Dynamic Vision Sensors (DVS) exhibit exceptional dynamic range and low power consumption, making them ideal for edge applications in the Internet of Video Things (IoVT). However, their output is often degraded by spurious Background Activity (BA) noise, leading to unnecessary computational overhead. This paper proposes SNNF, a near-sensor BA noise filter that integrates a compact Event-Based Binary Image (EBBI) representation, a parallel memory architecture, and a single-layer Spiking Neural Network (SNN) classifier. Trained on representative DVS data, the SNN distinguishes signal events from noise with an AUC of 0.89 on standard datasets. The binary-array-based EBBI eliminates timestamp dependency, significantly reducing memory footprint. Moreover, the SNN's spike-based computation replaces power-hungry multipliers with simple accumulation logic and minimizes inter-neuron data width, resulting in an extremely hardware-efficient design. FPGA implementation results show that SNNF reduces memory and logic resources to approximately 11% and 40%, respectively of state-of-the-art filters, while achieving a throughput of 29 Mega events per second (Meps). In a 65 nm CMOS ASIC implementation, SNNF achieves 44.4 Meps with an area and power consumption of only ~13% and <5% of the corresponding ANN-based designs. These results demonstrate that SNNF provides an excellent balance between filtering accuracy and hardware efficiency, making it highly suitable for resource-constrained, near-sensor deployment.