Cloud native EDA tools & pre-optimized hardware platforms
Author: YiTa Wu, Vice President of Engineering, ULSee
Deep learning is considered a more accurate tool than other machine learning strategies such as decision tree, genetic algorithm, and support vector machines. It stacks layers of perceptions to form a deep structure, and iteratively adjust the parameters by the backpropagation and gradient descent algorithm during the training procedure. However, to the end user, deep learning models are the “blackest” of all black boxes since it is very difficult to interpret how the system (neural network architecture) works. In this article, we will explain ULSee’s experience of designing a network architecture for multiple applications.
Figure 2. High computational complexity of heatmap design
The ULSee UL100 AI module integrates DesignWare® ARC® EV62 Processor IP and the neural network architecture discussed in this article to perform real-time facial recognition with very low power consumption. The chip performs edge computing for fast facial recognition and liveness detection. The module is deployed for ADAS and facial payments, smart door unlocking, and more.