{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T10:54:38Z","timestamp":1781866478885,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":18,"publisher":"ACM","license":[{"start":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T00:00:00Z","timestamp":1782086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,6,22]]},"DOI":"10.1145\/3797248.3815410","type":"proceedings-article","created":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T10:46:08Z","timestamp":1781865968000},"page":"42-47","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Energy Profiling of Pruned LLMs During Python Code Generation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5585-6699","authenticated-orcid":false,"given":"Pranav Reddy","family":"Danda","sequence":"first","affiliation":[{"name":"Webster University, Saint Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9477-5535","authenticated-orcid":false,"given":"Xue","family":"Li","sequence":"additional","affiliation":[{"name":"Webster University, Saint Louis, MO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,6,22]]},"reference":[{"key":"e_1_3_3_1_2_2","unstructured":"Francisco Caravaca \u00c1ngel Cuevas and Rub\u00e9n Cuevas. 2025. From Prompts to Power: Measuring the Energy Footprint of LLM Inference. arxiv:https:\/\/arXiv.org\/abs\/2511.05597\u00a0[cs.AI] https:\/\/arxiv.org\/abs\/2511.05597"},{"key":"e_1_3_3_1_3_2","unstructured":"Mark Chen et\u00a0al. 2021. Evaluating Large Language Models Trained on Code. arxiv:https:\/\/arXiv.org\/abs\/2107.03374\u00a0[cs.LG] https:\/\/arxiv.org\/abs\/2107.03374"},{"key":"e_1_3_3_1_4_2","unstructured":"Pranav\u00a0Reddy Danda. 2026. Github. https:\/\/github.com\/pranav0d\/IGSC2026"},{"key":"e_1_3_3_1_5_2","unstructured":"Pranav\u00a0Reddy Danda. 2026. HuggingFace. https:\/\/huggingface.co\/P11101"},{"key":"e_1_3_3_1_6_2","unstructured":"Pepijn de Reus Ana Oprescu and Jelle Zuidema. 2024. An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2. arxiv:https:\/\/arXiv.org\/abs\/2411.12758\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2411.12758"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Radoslav Desislavov Fernando Mart\u00ednez-Plumed and Jos\u00e9 Hern\u00e1ndez-Orallo. 2023. Trends in AI Inference Energy Consumption: Beyond the Performance-vs-Parameter Laws of Deep Learning. Sustainable Computing: Informatics and Systems 38 (2023) 100857.","DOI":"10.1016\/j.suscom.2023.100857"},{"key":"e_1_3_3_1_8_2","unstructured":"flytech. 2026. python-codes-25k. https:\/\/huggingface.co\/datasets\/flytech\/python-codes-25k"},{"key":"e_1_3_3_1_9_2","volume-title":"Advances in Neural Information Processing Systems","author":"Han Song","year":"2015","unstructured":"Song Han, Jeff Pool, John Tran, and William\u00a0J. Dally. 2015. Learning both Weights and Connections for Efficient Neural Networks. In Advances in Neural Information Processing Systems , Vol.\u00a028."},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","unstructured":"Erik\u00a0Johannes Husom Arda Goknil Merve Astekin Lwin\u00a0Khin Shar Andre Kasen Sagar Sen Benedikt\u00a0Andreas Mithassel and Ahmet Soylu. 2025. Sustainable LLM Inference for Edge AI: Evaluating Quantized LLMs for Energy Efficiency Output Accuracy and Inference Latency. ACM Trans. Internet Things 6 4 Article 28 (Nov. 2025) 35\u00a0pages. 10.1145\/3767742","DOI":"10.1145\/3767742"},{"key":"e_1_3_3_1_11_2","unstructured":"iamtarun. 2026. python-code-instructions-18k-alpaca. https:\/\/huggingface.co\/datasets\/iamtarun\/python_code_instructions_18k_alpaca"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Lo\u00efc Lannelongue Jason Grealey and Michael Inouye. 2021. Green Algorithms: Quantifying the Carbon Footprint of Computation. Advanced Science 8 12 (2021) 2100707.","DOI":"10.1002\/advs.202100707"},{"key":"e_1_3_3_1_13_2","first-page":"598","volume-title":"Advances in Neural Information Processing Systems","author":"LeCun Yann","year":"1989","unstructured":"Yann LeCun, John\u00a0S. Denker, and Sara\u00a0A. Solla. 1989. Optimal Brain Damage. In Advances in Neural Information Processing Systems , Vol.\u00a02. 598\u2013605."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","unstructured":"Pere Martra. 2024. Exploring GLU expansion ratios: Structured pruning in Llama-3.2 models. (2024). 10.31219\/osf.io\/qgxea","DOI":"10.31219\/osf.io\/qgxea"},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Pere Martra. 2025. Fragile Knowledge Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2. arxiv:https:\/\/arXiv.org\/abs\/2512.22671\u00a0[cs.CL] https:\/\/arxiv.org\/abs\/2512.22671","DOI":"10.36227\/techrxiv.176462159.96022593\/v2"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"David Patterson Joseph Gonzalez Urs H\u00f6lzle Quoc Le Chen Liang Lluis-Miquel Munguia Daniel Rothchild David\u00a0R So Maud Texier and Jeff Dean. 2022. The Carbon Footprint of Machine Learning Training Will Plateau Then Shrink. IEEE Computer 55 7 (2022) 18\u201328.","DOI":"10.1109\/MC.2022.3148714"},{"key":"e_1_3_3_1_17_2","unstructured":"Pol\u00a0G. Recasens Ferran Agullo Yue Zhu Chen Wang Eun\u00a0Kyung Lee Olivier Tardieu Jordi Torres and Josep\u00a0Ll. Berral. 2025. Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference. arxiv:https:\/\/arXiv.org\/abs\/2503.08311\u00a0[cs.DC] https:\/\/arxiv.org\/abs\/2503.08311"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1355"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","unstructured":"Samuel Williams Andrew Waterman and David Patterson. 2009. Roofline: an insightful visual performance model for multicore architectures. Commun. ACM 52 4 (April 2009) 65\u201376. 10.1145\/1498765.1498785","DOI":"10.1145\/1498765.1498785"}],"event":{"name":"IGSC '26: International Green and Sustainable Computing Conference","location":"Canandaigua USA","acronym":"IGSC 2026","sponsor":["SIGDA ACM Special Interest Group on Design Automation"]},"container-title":["Proceedings of the 16th ACM International Green and Sustainable Computing Conference"],"original-title":[],"deposited":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T10:47:12Z","timestamp":1781866032000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3797248.3815410"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,22]]},"references-count":18,"alternative-id":["10.1145\/3797248.3815410","10.1145\/3797248"],"URL":"https:\/\/doi.org\/10.1145\/3797248.3815410","relation":{},"subject":[],"published":{"date-parts":[[2026,6,22]]},"assertion":[{"value":"2026-06-22","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}