The recent developments in the research area are primarily focused on enhancing energy efficiency and performance stability across various computing platforms, particularly in edge devices and high-performance computing (HPC) systems. There is a notable trend towards leveraging advanced machine learning techniques, such as deep reinforcement learning (DRL) and reinforcement learning (RL), to dynamically manage and optimize resource allocation, thereby reducing latency and energy consumption. Innovations in hardware design, such as the use of superconducting quantum interference devices (SQUIDs) for cryogenic memory applications, are pushing the boundaries of energy efficiency and speed in quantum computing and HPC. Additionally, there is a growing emphasis on developing algorithms that can effectively manage unstructured sparse deep neural networks (DNNs) to maximize energy efficiency in compute-in-memory (CIM) crossbars. The field is also witnessing a shift towards more comprehensive benchmarking methodologies, such as MLPerf Power, to evaluate and optimize the energy efficiency of machine learning systems across a wide spectrum of power levels. These advancements collectively aim to address the critical challenges of performance variability, energy consumption, and thermal management in modern computing environments.

Noteworthy papers include one that introduces a novel framework for dynamically scaling CPU and GPU frequencies based on DRL to manage thermal and latency variations in edge devices, and another that proposes a multi-armed bandit approach for online energy optimization in GPUs, effectively balancing performance and energy trade-offs. Additionally, a paper on cryogenic ternary content addressable memory using ferroelectric SQUIDs stands out for its significant energy efficiency improvements in cryogenic applications.