AceleradorSNN: A Neuromorphic Cognitive System Integrating Spiking Neural Networks and Dynamic Image Signal Processing on FPGA
By Daniel Gutierrez, Ruben Martinez, Leyre Arnedo, Antonio Cuesta, Soukaina El Hamry
Intigia R&D Department, Alicante, Spain
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, Intigia has 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-gradient-trained SNN backbones, and the real-time streaming ISP ar chitecture implemented on Field-Programmable Gate Arrays (FPGA).
Index Terms
Spiking Neural Networks, FPGA, Dynamic Vi sion Sensor, Image Signal Processor, Neuromorphic Computing, Artificial Intelligence.
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