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

Daniel Gutierrez; Ruben Martinez; Leyre Arnedo; Antonio Cuesta; Soukaina El Hamry

cs.ARMar 30, 2026

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

Authors: Daniel Gutierrez, Ruben Martinez, Leyre Arnedo, Antonio Cuesta, Soukaina El Hamry

Organizations: 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, 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).

Neuromorphic Computing

Neuromorphic computing uses brain-inspired, event-driven algorithms and hardware to process information with low power and latency.

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Jul 6, 2026J/K move · Enter open · S save

Efficient Perception in Automotive Detection and Tracking Using Neuromorphic Computing

Manish Kolachalam, Rani Malhotra

Applied Research Center for Autonomous Machines, Infosys Center for Emerging Technologies, Bangalore, Karnataka, India, 560100

Energy-Efficient ML Autonomous Driving Perception Spiking Neural Networks Neuromorphic Computing Object Detection Multi-Object Tracking

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Deep learning algorithms are notorious for their high carbon footprint and computational demands that limit their deployment on edge devices and raise concerns about their long-term sustainability. Neuromorphic computing and Spiking Neural Networks (SNNs) offer a promising alternative to traditional Von Neumann architectures, providing energy-efficient performance, massively parallel computation, and on-chip learning capabilities. Autonomous machines represent a critical application domain where these advantages are particularly valuable. We present the first comprehensive evaluation of SNNs for real-world automotive multi-object detection and tracking. Using transfer learning with the SpikeYOLO architecture, we achieve mean Average Precision of 0.937 on the KITTI dataset and 0.771 on BDD100K MOT2020 dataset for object detection and a Higher Order Tracking Accuracy score of 0.701 (KITTI) and 0.445 (BDD100K MOT2020) for object tracking--results competitive with conventional deep learning methods. Our results demonstrate that SNNs can deliver high-performance object detection and tracking in an energy efficient manner, establishing their viability for perception in real-world autonomous systems.

Abstract

Neuromorphic Computing

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