Machine learning from the introduction of neural networks for embedded vision applications

**Author: Nick Ni and Adam Taylor** Machine learning is one of the most exciting fields in embedded vision today. It's not only shaping the future of vision systems but also influencing areas like industrial IoT and cloud computing. For those new to the concept, machine learning often involves creating and training neural networks. These networks are inspired by the human brain, with layers of interconnected nodes that process data step by step. At its core, a neuron receives input, applies weights, and then passes the result through an activation function. This process continues across multiple layers, forming what’s known as a feedforward neural network (FNN). If feedback loops are present, it becomes a recurrent neural network (RNN). Among these, deep neural networks (DNNs) are widely used due to their ability to handle complex tasks with multiple hidden layers. In the field of embedded vision, where systems are moving from basic vision-enabled setups to more advanced automation, convolutional neural networks (CNNs) play a crucial role. They’re designed to process 2D inputs like images, making them ideal for computer vision tasks. CNNs typically include convolutional layers, activation functions like ReLU, pooling layers for downsampling, and fully connected layers for final classification. Training a CNN requires a large dataset, including both correct and incorrect examples. The model learns by adjusting its internal parameters during this process. While training is usually done on powerful cloud processors, inference can be accelerated using hardware like FPGAs or GPUs. Frameworks such as Caffe and TensorFlow simplify the development of machine learning models. They provide pre-trained models, libraries, and tools that allow developers to build and train networks efficiently. Caffe, for instance, offers a model zoo where users can share and reuse models, saving time and effort. Xilinx’s reVISION stack further streamlines the integration of machine learning into embedded systems. It supports frameworks like OpenVX and Caffe, enabling efficient deployment of image processing and AI algorithms on heterogeneous SoCs. This approach leverages programmable logic for high performance and low latency, making it ideal for real-time applications. One key advantage of using fixed-point arithmetic, such as INT8, is improved efficiency without sacrificing much accuracy. This makes it suitable for resource-constrained environments. Xilinx’s reVISION stack takes full advantage of this, optimizing performance while reducing power consumption. In real-world scenarios, such as autonomous vehicle collision avoidance, the difference between traditional GPU-based solutions and FPGA-accelerated systems can be significant. A system using Xilinx’s UltraScale+ MPSoC can detect potential collisions in just 2.7ms, compared to 49ms–320ms for a GPU-based solution. This speed is critical for safety-critical applications. In conclusion, machine learning will continue to drive innovation in embedded systems, especially in robotics and automation. By combining powerful processors with flexible programmable logic, developers can create efficient, responsive, and scalable solutions. Tools like reVISION make it easier than ever to bring AI to the edge, opening up new possibilities for real-time applications.

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