optimize for energy efficiency

This work addresses the challenge in self-powered streaming networks where dynamic power management, while energy-efficient, incurs switching delays that degrade throughput and hinder real-time signal processing. The paper presents the first cycle-based scheduling framework tailored to such networks, formulating a linear program to compute the maximum achievable throughput and a mixed-integer linear program to minimize energy consumption under throughput constraints. To efficiently explore the trade-off between energy and throughput, the authors introduce a novel “Hop and Skip” multi-objective search strategy that rapidly generates a high-quality Pareto frontier. Experimental results demonstrate that the proposed approach significantly accelerates design space exploration on both benchmark and random graphs, and in practical case studies, it achieves superior energy-throughput trade-offs compared to always-on or purely self-powered baselines.

Power optimization for exascale supercomputing remains challenging, particularly under the heterogeneous GH200 superchip architecture. Method: This work proposes a CPU–GPU collaborative runtime dynamic power management framework, introducing a novel speed–energy–latency joint metric model and a Euclidean-distance-based multi-objective optimization scheme. It achieves, for the first time, fine-grained GPU task-level power control integrated with holistic CPU–GPU power orchestration. Contribution/Results: Evaluated on the LSMS scientific application, the method demonstrates that moderate GPU power reduction preserves computational performance while significantly improving system energy efficiency—achieving 12.7% global energy savings with only marginal latency overhead (<3.2%). This work establishes a scalable methodology and empirical foundation for adaptive energy-efficiency optimization in exascale systems.

This study addresses the challenge of dynamic scheduling in batteryless IoT systems, where traditional approaches relying on static thresholds or hardware-specific models struggle under highly variable energy availability and workload dynamics. The work proposes two hardware-agnostic, dynamic scheduling strategies that operate without prior knowledge of energy consumption: a model-free reinforcement learning (RL) agent and an online approximate prediction (AP) method, both treating applications as black boxes. To the best of our knowledge, this is the first demonstration of black-box dynamic scheduling in batteryless IoT that is independent of hardware characteristics, systematically uncovering the trade-offs among task throughput, node survivability, and execution pacing. Experimental results show that AP closely approaches oracle-level performance, RL flexibly balances energy usage and survival rate, and adaptive task-rate control (AsTAR) excels under prolonged energy outages, while devices with large capacitors can still benefit from static strategies.

This study addresses the energy–latency–reliability trade-off in data compression and transmission for resource-constrained wireless devices in edge computing. We propose an application-driven end-to-end latency budgeting mechanism, departing from conventional hard real-time constraints. A joint optimization model is formulated, with compression ratio and device processing speed as key decision variables, to characterize their nonlinear interdependencies and compute the Pareto-optimal frontier. Theoretical analysis and experiments demonstrate that modest relaxation of end-to-end latency yields exponential reductions in energy consumption—minor latency increases enable substantial energy savings. The proposed framework provides a quantifiable, configurable design paradigm for low-power, adaptive edge communication, while rigorously guaranteeing reliability requirements.

High energy consumption in data centers constrains the development of energy technologies. Method: This project introduces a novel paradigm—“energy-research-dedicated HPC integrated with deep energy-efficiency co-design”—moving beyond conventional PUE-centric optimization to systematically integrate high-efficiency computing architectures, energy-system modeling, and data-driven energy-efficiency assessment methodologies. Leveraging the National Renewable Energy Laboratory’s (NREL) one of the world’s most energy-efficient supercomputing facilities, it supports U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy (EERE) research across power generation, energy efficiency, transportation, and systems modeling. Contribution/Results: Over ten years, computational workload increased 30-fold while maintaining high performance. The project empirically validates research-oriented green data centers as critical “accelerators” for energy technology innovation, significantly reducing both operational costs and lifecycle environmental impact. It delivers a transferable methodology and practical blueprint for sustainable computing infrastructure.

This work proposes the first application-agnostic CPU power management approach based on offline reinforcement learning, circumventing the challenges of online methods—such as difficulties in environment modeling, system disturbances, and safety risks. By leveraging historical policy data, the controller is trained without requiring prior knowledge of target applications, utilizing readily available system signals including Intel RAPL power measurements, hardware performance counters, and runtime heartbeat indicators. The method achieves generalizable energy efficiency across diverse workloads while maintaining computational reliability. Experimental evaluation on a range of compute- and memory-intensive benchmarks demonstrates substantial energy savings with only modest and acceptable performance overhead, effectively balancing scientific computing fidelity and energy conservation.

This work proposes an adaptive metaheuristic optimization framework that maximizes fitness gains under a fixed energy budget by leveraging operator-level energy efficiency. Introducing the Expected Improvement per Joule (EI/J) metric, the framework dynamically schedules lightweight and heavyweight operators to balance exploration and exploitation while optimizing energy utilization. For the first time, energy efficiency is explicitly integrated into the operator selection mechanism within a steady-state evolutionary algorithm that combines genetic operators, particle swarm optimization, and iterative local search. Evaluated on three combinatorial optimization problems—knapsack instances, NK landscapes, and error-correcting code design—the approach significantly reduces energy consumption compared to baseline methods while maintaining comparable solution quality. Empirical results further show that EI/J values converge early, leading to stable and reliable operator selection, thereby demonstrating the strategy’s generalizability across diverse problem domains.

This study addresses the multi-objective trade-offs among generation quality, energy consumption, latency, and memory when deploying large language models on edge devices. The authors construct a reproducible empirical evaluation framework to systematically analyze the energy efficiency, performance, and privacy characteristics of models ranging from 0.5B to 9B parameters on a real-world Android device (Samsung Galaxy S25 Ultra). Leveraging non-intrusive, fine-grained power monitoring and mixed-precision inference, they uncover a “quantization-energy paradox”: model architecture—not quantization strategy—dominates energy consumption. Notably, Mixture-of-Experts architectures disrupt conventional scaling–energy relationships, and medium-scale models such as Qwen2.5-3B emerge as the optimal choice, balancing high output quality with energy efficiency, thereby offering practical deployment guidelines for on-device intelligence.

In the post-Dennard era, embedded systems face intricate trade-offs between energy efficiency and latency, rendering traditional heuristic methods ineffective in navigating the high-dimensional, non-smooth scheduling space. This work proposes a Gaussian process-based multi-objective Bayesian optimization framework to automatically discover Pareto-optimal scheduling strategies that balance energy consumption and execution time on heterogeneous multicore architectures. By integrating fANOVA sensitivity analysis and comparing multiple covariance kernels—such as Matérn and RBF—the approach endows the black-box optimizer with physical interpretability, uncovering how key hardware parameters influence system performance. Experimental results demonstrate that the method efficiently approximates the Pareto front, significantly advancing both the automation of scheduling and the understanding of underlying hardware behaviors.

This study addresses the challenge of optimizing server energy efficiency in high-throughput computing environments, where performance and energy consumption are often at odds. Leveraging real-world operational data and targeted experiments, the work systematically investigates how server configurations influence power consumption, performance, and carbon emissions, uncovering key barriers to implementing effective energy-saving measures in practice. Through empirical power monitoring, workload modeling, and carbon footprint assessment, the authors identify critical factors governing energy efficiency and propose a practical configuration strategy that simultaneously ensures performance guarantees and advances low-carbon objectives. Evaluated under representative high-throughput workloads, the proposed approach achieves substantial reductions in both energy use and carbon emissions.