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).

翻译：自动驾驶系统——如高级驾驶辅助系统（ADAS）、无人驾驶飞行器（UAVs）和工业4.0机器人——对高速、低延迟及高能效目标检测的需求，暴露了传统卷积神经网络（CNNs）的局限性。为应对这些挑战，我们开发了第三代人工智能认知系统AceleradorSNN。该架构集成了基于脉冲神经网络（SNN）的神经形态处理单元（NPU）以处理来自动态视觉传感器（DVS）的异步数据，以及一个面向RGB摄像头的动态可重构认知图像信号处理器（ISP）。本文详细介绍了两个IP核的硬件导向设计、基于替代梯度的SNN骨干网络评估，以及在现场可编程门阵列（FPGA）上实现的实时流式ISP架构。