Energy Efficient Computing Systems: Architectures, Abstractions and Modeling to Techniques and Standards
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Abstract (arXiv)
Computing systems have undergone several inflexion points - while Moore's law guided the semiconductor industry to cram more and more transistors and logic into the same volume, the limits of instruction-level parallelism (ILP) and the end of Dennard's scaling drove the industry towards multi-core chips. We have now entered the era of domain-specific architectures for new workloads like AI and ML. These trends continue, arguably with other limits, along with challenges imposed by tighter integration, extreme form factors and diverse workloads, making systems more complex from an energy efficiency perspective. Many research surveys have covered different aspects of techniques in hardware and microarchitecture across devices, servers, HPC, data center systems along with software, algorithms, frameworks for energy efficiency and thermal management. Somewhat in parallel, the semiconductor industry has developed techniques and standards around specification, modeling and verification of complex chips; these areas have not been addressed in detail by previous research surveys. This survey aims to bring these domains together and is composed of a systematic categorization of key aspects of building energy efficient systems - (a) specification - the ability to precisely specify the power intent or properties at different layers (b) modeling and simulation of the entire system or subsystem (hardware or software or both) so as to be able to perform what-if analysis, (c) techniques used for implementing energy efficiency at different levels of the stack, (d) verification techniques used to provide guarantees that the functionality of complex designs are preserved, and (e) energy efficiency standards and consortiums that aim to standardize different aspects of energy efficiency, including cross-layer optimizations.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 20 Jul 2020.
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Citing papers per year: 2024: 2; 2025: 2; 2026: 2.
6 papers.
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- 2024Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivitycited by 0
- 2025Efficiency Will Not Lead to Sustainable Reasoning AIcited by 0
- 2025How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Studycited by 0
- 2026Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Servingcited by 0
- 2026Dissecting the Carbon-SLO Tension in Geo-Distributed LLM Serving10 Oct 2026
- 2026LLMs in Digital EDA: A perspective on shifting roles from Generation to Orchestration27 Aug 2026
- 2025How Do Agentic AI Systems Deal With Software Energy Concerns? A Pull Request-Based Study31 Dec 2025
- 2025Efficiency Will Not Lead to Sustainable Reasoning AI19 Nov 2025
- 2024Recommender Systems for Sustainability: Overview and Research Issues4 Dec 2024
- 2024Idle is the New Sleep: Configuration-Aware Alternative to Powering Off FPGA-Based DL Accelerators During Inactivity28 Jun 2024