What it is

Energy-efficient computing is the design and operation of hardware, systems, and software with the explicit goal of reducing electrical energy consumption while delivering required performance. It spans many layers of the stack: energy-aware chip microarchitecture (low-power cores, DVFS, specialized accelerators), power-proportional datacenter servers and cooling, operating-system and compiler techniques that trade performance for energy, runtime task schedulers and resource managers that minimise energy per task, and application-level approaches (model pruning, quantization, efficient ML architectures). The discipline brings together computer architecture, systems software, electrical engineering, control theory and sustainability science to make computation more cost-effective and environmentally sustainable.

Applications

• Cloud & hyperscaler datacenters: energy-aware server provisioning, efficient cooling and power distribution, rack/VM consolidation, and hardware accelerators for ML inference/training.
• AI accelerators & chips: domain-specific accelerators (TPUs, NPU, low-precision tensor engines), systolic arrays and mixed-precision units that reduce joules per operation.
• Mobile devices & wearables: power-optimized SoCs, dynamic frequency/voltage scaling, and software techniques that extend battery life.
• IoT & edge devices: tiny ML, microcontrollers with aggressive sleep modes, and energy-harvesting systems for long-life sensing.
• High-performance and scientific computing: energy-aware scheduling, power capping, and co-design to keep exascale systems within facility power budgets.

Future potential

• Energy-aware AI & model design. Training and inference pipelines will routinely optimise for energy per inference/training-step (not just accuracy), leading to model families and training recipes that are “green by default.”
• Serverless + carbon-aware scheduling. Cloud platforms will schedule workloads by time/region to reduce carbon intensity and overall energy (e.g., shifting non-urgent jobs to lower-carbon grid times).
• Holistic hardware-software co-design. New accelerators, compilers and runtimes co-optimise performance, latency and energy; verification and benchmarks will include “joules per task” as a first-class metric.
• Standards & transparency. Industry standards for reporting energy/efficiency (and possibly regulatory disclosure) will make energy performance a public, comparable metric for vendors and services.

2) Current research areas in Energy-Efficient Computing

1. Low-power microarchitecture & accelerators: design of energy-proportional cores, heterogeneous compute fabrics, and domain-specific accelerators (AI/ML, graph processing) to reduce energy per operation.
2. Dynamic Voltage and Frequency Scaling (DVFS) & power management: run-time controllers and policies that trade latency or throughput for energy savings.
3. Energy-aware compilers and runtime systems: compiler optimizations, power-aware scheduling, and OS-level policies that minimize energy for workloads.
4. Efficient machine learning (tinyML / model compression): pruning, quantization, knowledge distillation, NAS (neural architecture search) with energy as an objective.
5. Data-centre energy management & cooling: workload placement, thermal management, free-cooling strategies, and liquid cooling to improve PUE (power usage effectiveness).
6. Hardware/software co-design and approximate computing: trading exactness for lower energy in tolerant applications (multimedia, perception).
7. Energy-efficient networking and communication: green networking protocols, low-power wireless for IoT, and energy-aware routing for distributed systems.
8. Measurement, benchmarking & life-cycle analysis: standardized metrics (joules/op, energy per inference), reproducible measurement tools, and cradle-to-grave carbon accounting for hardware.
9. Energy harvesting and battery-less systems: circuits and system designs that operate from harvested energy for long-lived edge deployments.
10. Policy, economics & sustainability modelling: cost/benefit analyses, grid interactions, and policy frameworks for incentivizing energy-efficient designs.

3) Key journals that publish Energy-Efficient Computing research

Below are three or more journals in each category (Open / Hybrid / Subscription). For each journal I note indexing in Scopus where available and give a short “fit” note.

Note about “Scopus and CSI Tools”: I checked Scopus indexing status for the titles below using publisher and indexing pages. If by CSI Tools you meant a library/journal-evaluation resource (for example CUNY CSI’s “Evaluate Journals”), that is useful for cross-checking indexing and quality. If you meant a different commercial product called “CSI Tools,” tell me which one and I’ll adapt.

Open-access (Gold OA) journals

1. Energies (MDPI) — Open access; publishes on energy-related tech including energy efficiency in computing and datacentres. Indexed by Scopus. Good for applied and interdisciplinary work.
2. Sustainability (MDPI) — Open access; broad sustainability topics including sustainable ICT, green computing studies. Indexed in Scopus. Useful for cross-disciplinary sustainability analyses and lifecycle studies.
3. ACM Transactions on Architecture and Code Optimization (TACO) — ACM lists TACO as supporting open access options; it publishes architecture/optimization research relevant to energy-efficient design and is indexed in Scopus. Good for rigorous architecture and compiler co-design papers.

Hybrid journals

1. IEEE Transactions on Sustainable Computing — Hybrid; explicitly focused on sustainable and energy-aware computing topics. Indexed in Scopus. Good fit for hardware/software co-design and systems research.
2. Sustainable Computing: Informatics and Systems (Elsevier) — Hybrid; dedicated to energy-aware and thermal-aware computing, datacenter efficiency and related areas. Indexed in Scopus/SCImago. Good for specialized sustainable-computing work.
3. Journal of Parallel and Distributed Computing (Elsevier) — Hybrid; publishes energy-efficient scheduling, parallel algorithms for low energy, and datacenter studies. Indexed in Scopus. Good for distributed/system-level contributions.

Subscription / traditional (paywalled or society journals; may have OA options)

1. IEEE Transactions on Green Communications and Networking — Subscription/hybrid; focuses on energy-efficient networking and communications, indexed in Scopus. Good for networking-oriented energy research.
2. International Journal of Green Computing (IGI Global) — Often subscription/hybrid; focuses on green IT and sustainable computing topics (check publisher page for OA options). Indexed in some bibliographic services; check Scopus for current coverage.
3. IEEE Transactions on Computers / IEEE Journal of Solid-State Circuits (for chip designers) — Traditional society journals with high standards; suitable for low-power microarchitecture and circuit-level energy work (each has Scopus coverage). Choose the venue that best matches the level (architectural vs circuit).