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 talked-about topics in the field of embedded vision today. Its influence extends far beyond just embedded vision, touching areas like the industrial Internet of Things (IIoT) 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 each "neuron" receiving input, processing it, and passing it along. A basic neural network consists of an input layer, multiple hidden layers, and an output layer. At its core, a neuron takes input values, applies weights, and then applies a transfer function to produce an output. This output is passed to the next layer or directly to the final output. Neural networks can be categorized into different types based on their structure. Feedforward neural networks (FNNs) do not have loops, while recurrent neural networks (RNNs) do. One of the most widely used types is the Deep Neural Network (DNN), which has multiple hidden layers, enabling more complex tasks. Training these networks requires a large dataset and careful tuning of weights and biases. In applications like embedded vision, where two-dimensional inputs such as images are common, Convolutional Neural Networks (CNNs) are preferred. CNNs are a type of feedforward network that uses convolutional layers to detect features, followed by pooling layers to reduce data size, and fully connected layers for classification. The weights in a CNN are learned through training, typically using a large set of labeled images. Training is usually done on high-performance cloud systems due to the computational demands. Once trained, the model can be deployed on embedded platforms for real-time inference. To simplify development, frameworks like Caffe and TensorFlow provide pre-trained models and tools for building and training networks. Caffe, for example, allows developers to use pre-trained weights and share models through a model zoo, making it easier to implement custom solutions. Xilinx's reVISION stack enables efficient implementation of machine learning and image processing on heterogeneous SoCs like the Zynq UltraScale+ MPSoC. It integrates with frameworks like Caffe and supports hardware acceleration for faster inference. Using programmable logic, image processing pipelines can be optimized, reducing latency and power consumption. The choice of numerical representation also plays a key role in performance. Many applications now use INT8 precision instead of FP32, offering a good balance between speed and accuracy. This is especially beneficial in programmable logic, where specialized DSP blocks can efficiently handle fixed-point operations. In real-world applications, such as autonomous vehicle collision avoidance systems, the benefits of using reVISION become clear. Compared to GPU-based approaches, the reVISION design achieves significantly lower latency, detecting potential collisions much faster. This difference can mean the difference between avoiding an accident and not. In summary, machine learning will continue to drive innovation in fields like robotics and automation. Heterogeneous SoCs combined with powerful software stacks like reVISION offer efficient, flexible, and scalable solutions for embedded vision and machine learning applications.

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