A Machine Learning-Based Strategy for Efficient Resource Management of Video Encoding on Heterogeneous MPSoCs

The design of new streaming systems is becoming a major area of research to deploy services targeted in the Internet-of-Things (IoT) era. In this context, the new High Efficiency Video Coding (HEVC) standard provides high efficiency and scalability of quality at the cost of increased computational complexity for edge nodes, which is a new challenge for the design of IoT systems. The usage of hardware acceleration in conjunction with general-purpose cores in Multiprocessor Systems-on-Chip (MPSoCs) is a promising solution to create heterogeneous computing systems to manage the complexity of real-time streaming for high-end IoT systems, achieving higher throughput and power efficiency when compared to conventional processors alone. Furthermore, Machine Learning (ML) provides a promising solution to efficiently use this next-generation of heterogeneous MPSoC designs that the EDA industry is developing by dynamically optimizing system performance under diverse requirements such as frame resolution, search area, operating frequency and stream allocation. In this work, we propose an ML-based approach for stream allocation and Dynamic Voltage and Frequency Scaling (DVFS) management on a heterogeneous MPSoC composed of ARM cores and FPGA fabric containing hardware accelerators for the motion estimation of HEVC encoding. Our experiments on a Zynq7000 SoC outline 20% higher throughput when compared to the state-of-the-art streaming systems for next-generation IoT devices.