VLSI Design of an ML-Based Power-Efficient Motion Estimation Controller for Intelligent Mobile Systems

In this paper, a machine learning (ML)-based power-efficient motion estimation (ME) controller algorithm and VLSI architecture incorporating coding bandwidth and rate-distortion (R-D) cost using convex optimization are proposed to effectuate a smart and bandwidth-efficient ME design for intelligent mobile systems. To be smart and adapt to time-altering coding bandwidth using intelligent power-management techniques in modern application processor systems, we first propose an ML-based bandwidth-on-demand ME controller algorithm based on the convex optimization method to resolve the lack of an awareness of coding bandwidth in prior ME designs. Then, a hardware-friendly and power-efficient VLSI architecture is developed to implement an intelligent, high-performance, and low-power ME controller design that can be combined with prior ME designs to satisfy the bandwidth-efficient ME design target under bandwidth constraints. The final implementation results show that the proposed smart ME controller architecture using our proposed bandwidth control scheme costs 0.816K gate counts, consumes 0.873 mW of power at a working frequency of 1.1 GHz with Taiwan Semiconductor Manufacture Company (TSMC) 90-nm CMOS technology, and achieves an average bandwidth reduction of 56.08% compared with previous non-bandwidth-on-demand ME designs for high-definition (HD) videos.