CSR: Small: Evolution of Computer Vision for Low Power Devices, Breaking its Power Wall and Computational Complexity

The accuracy of computer vision for object recognition and classification has surpassed human capabilities. Adoption of brain-inspired Convolutional Neural Network (CNN) models and the ability to train and execute these complex networks by modern graphical processing units (GPUs) are the backbone of this progress. However, in terms of computational requirement, memory usage, and power consumption, the CNN solutions are extremely demanding.

Meanwhile, many interesting applications of computer vision - such as small robotics, a wide range of Cyber-Physical Systems, and many smart devices on the Internet of Things - are resource constrained. This project aims to substantially lower the computational complexity, the average-case classification power and the latency of CNN-based vision, enabling its deployment to a much wider range of platforms.

From a societal viewpoint, this study enhances the research, education, and diversity at George Mason University (GMU) by involving graduate, undergraduate, minority and female students, and enriches several courses that are offered at GMU.

The goals of this research project are as follows: (1) Reformulating the CNN-based learning model into an Iterative Convolutional Neural Network (ICNN) learning model that allows early classification and permits early termination via various thresholding mechanisms and developing a framework to use the contextual knowledge that could be extracted from earlier iterations to guide and reduce the computation of future iterations. (2) Developing an approximate ICNN coprocessor that supports approximation in memory and logic by exploring new approximation opportunities created by ICNN, and enhancing the ICNN to adjust and learn the approximate hardware behavior in addition to its intended functionality.