Energy-Efficient Mobile Computer Vision and Machine Learning Processors
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Technology scaling has driven computing devices to be faster, cheaper, and smaller while consuming less power in past decades. However, as technology scaling has become increasingly difficult in recent years, power has become the major constraint in performance, and thus, the improvement in the performance of mobile devices has begun to diminish. Moreover, emerging intelligent mobile systems are demanding increasing computing power. In light of this challenge associated with artificial intelligence, domain-specific architectures are widely believed to be the path to realizing considerable improvements in the efficiency, performance and cost of intelligent mobile systems.
This dissertation presents several algorithm, architecture and circuit co-optimized solutions for intelligent and autonomous mobile systems, including vision-based stereo depth, optical flow, simultaneous localization and mapping (SLAM) and convolutional neural network- (CNN)-based image recognition. Together, these solutions enable the mobile systems to form a geometric and semantic understanding of the environment. Various optimizations including parallelism, scheduling, exploiting sparsity and circuit customization are applied to overcome the complexity of these problems for energy-efficient, real-time, robust operation.