AceleradorSNN: A Neuromorphic Cognitive System Integrating Spiking Neural Networks and DynamicImage Signal Processing on FPGA

TL;DR

AceleradorSNN is a neuromorphic cognitive system on FPGA that combines SNNs for DVS data and a reconfigurable ISP for RGB images, enabling efficient object detection in autonomous systems.

Contribution

It introduces a hardware design integrating SNNs and dynamic image processing on FPGA, optimized for real-time autonomous applications.

Findings

Implemented surrogate-gradient-trained SNN backbones.

Designed real-time streaming FPGA-based ISP architecture.

Demonstrated high-speed, low-latency object detection capabilities.

Abstract

The demand for high-speed, low-latency, and energy-efficient object detection in autonomous systems -- such as advanced driver-assistance systems (ADAS), unmanned aerial vehicles (UAVs), and Industry 4.0 robotics -- has exposed the limitations of traditional Convolutional Neural Networks (CNNs). To address these challenges, we have developed AceleradorSNN, a third-generation artificial intelligence cognitive system. This architecture integrates a Neuromorphic Processing Unit (NPU) based on Spiking Neural Networks (SNNs) to process asynchronous data from Dynamic Vision Sensors (DVS), alongside a dynamically reconfigurable Cognitive Image Signal Processor (ISP) for RGB cameras. This paper details the hardware-oriented design of both IP cores, the evaluation of surrogate-gradienttrained SNN backbones, and the real-time streaming ISP architecture implemented on Field-Programmable Gate Arrays (FPGA).