Energy characterization and instruction-level energy model of Intel's Xeon Phi processor

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Chasing Carbon: The Elusive Environmental Footprint of Computing (2020, background): “Although optimizing nonfunctional properties, such as minimizing energy consumption through instruction-level energy annotation [101]–[103], can indirectly mitigate environmental impact, future compiler and programming-language technologies can optimize for CO 2 emissions directly by applying our…”
Estimation of energy consumption in machine learning (2019, methodology): “Most of the approaches run a set of curated micro-benchmarks where each benchmark loops over a target instruction type, to be able to isolate the power of that specific instruction [51,66,76].”
Data Center Energy Consumption Modeling: A Survey (2016, methodology): “developed an instruction-level energy model for Intel Xeon Phi processor which is the first commercial many core/multi-thread x86-based processor [171].”
Understanding the Future of Energy Efficiency in Multi-Module GPUs (2019, methodology): “Overall, GPUJoule’s estimation and validation process is fairly comparable to prior works that focus on instruction-based energy estimation in the server [27], mobile [28], and many-core architecture [26] domains but adjusted for the unique properties of GPU architectures.”