{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T08:00:45Z","timestamp":1776931245023,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":86,"publisher":"ACM","funder":[{"name":"Technology Innovation Institute"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,10,18]]},"DOI":"10.1145\/3725843.3756113","type":"proceedings-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T17:19:56Z","timestamp":1760721596000},"page":"854-868","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Amove: Accelerating LLMs through Mitigating Outliers and Salient Points via Fine-Grained Grouped Vectorized Data Type"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-9988-2940","authenticated-orcid":false,"given":"Xilong","family":"Xie","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6112-1928","authenticated-orcid":false,"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9438-9181","authenticated-orcid":false,"given":"Limin","family":"Xiao","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8085-9047","authenticated-orcid":false,"given":"Meng","family":"Han","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4854-7382","authenticated-orcid":false,"given":"Lei","family":"Liu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7650-5169","authenticated-orcid":false,"given":"Xiangrong","family":"Xu","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6690-8386","authenticated-orcid":false,"given":"Jinquan","family":"Wang","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3099-6078","authenticated-orcid":false,"given":"Zhen","family":"Song","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7924-9268","authenticated-orcid":false,"given":"Xiaojian","family":"Liao","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Mohammad\u00a0Sadegh Akhondzadeh Aleksandar Bojchevski Evangelos Eleftheriou and Martino Dazzi. 2025. KurTail: Kurtosis-based LLM Quantization. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2503.01483 (2025).","DOI":"10.18653\/v1\/2025.findings-emnlp.943"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6239"},{"key":"e_1_3_3_1_4_2","unstructured":"Yelysei Bondarenko Markus Nagel and Tijmen Blankevoort. 2023. Quantizable transformers: Removing outliers by helping attention heads do nothing. Advances in Neural Information Processing Systems 36 (2023) 75067\u201375096."},{"key":"e_1_3_3_1_5_2","unstructured":"Tom Brown Benjamin Mann Nick Ryder Melanie Subbiah Jared\u00a0D Kaplan Prafulla Dhariwal Arvind Neelakantan Pranav Shyam Girish Sastry Amanda Askell et\u00a0al. 2020. Language models are few-shot learners. Advances in neural information processing systems 33 (2020) 1877\u20131901."},{"key":"e_1_3_3_1_6_2","unstructured":"Karthik Chandrasekar Christian Weis Yonghui Li Benny Akesson Norbert Wehn and Kees Goossens. 2012. DRAMPower: Open-source DRAM power & energy estimation tool. URL: http:\/\/www. drampower. info 22 (2012)."},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Yupeng Chang Xu Wang Jindong Wang Yuan Wu Linyi Yang Kaijie Zhu Hao Chen Xiaoyuan Yi Cunxiang Wang Yidong Wang et\u00a0al. 2024. A survey on evaluation of large language models. ACM transactions on intelligent systems and technology 15 3 (2024) 1\u201345.","DOI":"10.1145\/3641289"},{"key":"e_1_3_3_1_8_2","unstructured":"Yuzong Chen Ahmed\u00a0F AbouElhamayed Xilai Dai Yang Wang Marta Andronic George\u00a0A Constantinides and Mohamed\u00a0S Abdelfattah. 2024. BitMoD: Bit-serial Mixture-of-Datatype LLM Acceleration. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2411.11745 (2024)."},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"crossref","unstructured":"Yu-Hsin Chen Tushar Krishna Joel\u00a0S Emer and Vivienne Sze. 2016. Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE journal of solid-state circuits 52 1 (2016) 127\u2013138.","DOI":"10.1109\/JSSC.2016.2616357"},{"key":"e_1_3_3_1_10_2","unstructured":"Wei-Lin Chiang Zhuohan Li Ziqing Lin Ying Sheng Zhanghao Wu Hao Zhang Lianmin Zheng Siyuan Zhuang Yonghao Zhuang Joseph\u00a0E Gonzalez et\u00a0al. 2023. Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality. See https:\/\/vicuna. lmsys. org (accessed 14 April 2023) 2 3 (2023) 6."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Davide Chicco Matthijs\u00a0J Warrens and Giuseppe Jurman. 2021. The coefficient of determination R-squared is more informative than SMAPE MAE MAPE MSE and RMSE in regression analysis evaluation. Peerj computer science 7 (2021) e623.","DOI":"10.7717\/peerj-cs.623"},{"key":"e_1_3_3_1_12_2","unstructured":"Aakanksha Chowdhery Sharan Narang Jacob Devlin Maarten Bosma Gaurav Mishra Adam Roberts Paul Barham Hyung\u00a0Won Chung Charles Sutton Sebastian Gehrmann et\u00a0al. 2023. Palm: Scaling language modeling with pathways. Journal of Machine Learning Research 24 240 (2023) 1\u2013113."},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"crossref","unstructured":"Cu Cui. 2024. Acceleration of tensor-product operations with tensor cores. ACM Transactions on Parallel Computing 11 4 (2024) 1\u201324.","DOI":"10.1145\/3695466"},{"key":"e_1_3_3_1_14_2","unstructured":"Steve Dai Rangha Venkatesan Mark Ren Brian Zimmer William Dally and Brucek Khailany. 2021. Vs-quant: Per-vector scaled quantization for accurate low-precision neural network inference. Proceedings of Machine Learning and Systems 3 (2021) 873\u2013884."},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3579371.3589351"},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"Lawrence\u00a0T DeCarlo. 1997. On the meaning and use of kurtosis.Psychological methods 2 3 (1997) 292.","DOI":"10.1037\/1082-989X.2.3.292"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Yihe Deng Pan Lu Fan Yin Ziniu Hu Sheng Shen Quanquan Gu James\u00a0Y Zou Kai-Wei Chang and Wei Wang. 2024. Enhancing large vision language models with self-training on image comprehension. Advances in Neural Information Processing Systems 37 (2024) 131369\u2013131397.","DOI":"10.52202\/079017-4175"},{"key":"e_1_3_3_1_18_2","unstructured":"Tim Dettmers Mike Lewis Younes Belkada and Luke Zettlemoyer. 2022. LLM. int8 (): 8-bit Matrix Multiplication for Transformers at Scale. CoRR abs\/2208.07339 (2022)."},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"Jesse Dodge Maarten Sap Ana Marasovi\u0107 William Agnew Gabriel Ilharco Dirk Groeneveld Margaret Mitchell and Matt Gardner. 2021. Documenting large webtext corpora: A case study on the colossal clean crawled corpus. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2104.08758 (2021).","DOI":"10.18653\/v1\/2021.emnlp-main.98"},{"key":"e_1_3_3_1_20_2","unstructured":"Chao Fang Man Shi Robin Geens Arne Symons Zhongfeng Wang and Marian Verhelst. 2024. Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2411.15982 (2024)."},{"key":"e_1_3_3_1_21_2","unstructured":"Elias Frantar Saleh Ashkboos Torsten Hoefler and Dan Alistarh. 2022. Gptq: Accurate post-training quantization for generative pre-trained transformers. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2210.17323 (2022)."},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"crossref","unstructured":"Amir Gholami Zhewei Yao Sehoon Kim Coleman Hooper Michael\u00a0W Mahoney and Kurt Keutzer. 2024. AI and memory wall. IEEE Micro (2024).","DOI":"10.1109\/MM.2024.3373763"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1145\/3579371.3589038"},{"key":"e_1_3_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO56248.2022.00095"},{"key":"e_1_3_3_1_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO61859.2024.00118"},{"key":"e_1_3_3_1_26_2","unstructured":"Dan Hendrycks Collin Burns Steven Basart Andy Zou Mantas Mazeika Dawn Song and Jacob Steinhardt. 2020. Measuring massive multitask language understanding. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2009.03300 (2020)."},{"key":"e_1_3_3_1_27_2","unstructured":"Weiming Hu Haoyan Zhang Cong Guo Yu Feng Renyang Guan Zhendong Hua Zihan Liu Yue Guan Minyi Guo and Jingwen Leng. 2025. M-ANT: Efficient Low-bit Group Quantization for LLMs via Mathematically Adaptive Numerical Type. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2502.18755 (2025)."},{"key":"e_1_3_3_1_28_2","unstructured":"Wei Huang Yangdong Liu Haotong Qin Ying Li Shiming Zhang Xianglong Liu Michele Magno and Xiaojuan Qi. 2024. Billm: Pushing the limit of post-training quantization for llms. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2402.04291 (2024)."},{"key":"e_1_3_3_1_29_2","unstructured":"Xijie Huang Zhiqiang Shen Pingcheng Dong and Kwang-Ting Cheng. 2023. Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2307.00331 (2023)."},{"key":"e_1_3_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00286"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-8844-3_4"},{"key":"e_1_3_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA57654.2024.00064"},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"crossref","unstructured":"Chunxu Ji and Amber\u00a0L Puha. 2025. Heavy traffic scaling limits for shortest remaining processing time queues with light tailed processing time distributions. Queueing Systems 109 1 (2025) 1\u201358.","DOI":"10.1007\/s11134-024-09929-8"},{"key":"e_1_3_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1145\/3079856.3080246"},{"key":"e_1_3_3_1_35_2","unstructured":"Dongyun Kam Myeongji Yun Sunwoo Yoo Seungwoo Hong Zhengya Zhang and Youngjoo Lee. 2024. Panacea: Novel DNN Accelerator using Accuracy-Preserving Asymmetric Quantization and Energy-Saving Bit-Slice Sparsity. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.10059 (2024)."},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCA45697.2020.00047"},{"key":"e_1_3_3_1_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3695053.3731019"},{"key":"e_1_3_3_1_38_2","doi-asserted-by":"crossref","unstructured":"Yoongu Kim Weikun Yang and Onur Mutlu. 2015. Ramulator: A fast and extensible DRAM simulator. IEEE Computer architecture letters 15 1 (2015) 45\u201349.","DOI":"10.1109\/LCA.2015.2414456"},{"key":"e_1_3_3_1_39_2","volume-title":"Logic synthesis using Synopsys\u00ae","author":"Kurup Pran","year":"1997","unstructured":"Pran Kurup and Taher Abbasi. 1997. Logic synthesis using Synopsys\u00ae. Springer Science & Business Media."},{"key":"e_1_3_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i12.29237"},{"key":"e_1_3_3_1_41_2","doi-asserted-by":"crossref","unstructured":"Jungi Lee Wonbeom Lee and Jaewoong Sim. 2024. Tender: Accelerating Large Language Models via Tensor Decomposition and Runtime Requantization. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2406.12930 (2024).","DOI":"10.1109\/ISCA59077.2024.00080"},{"key":"e_1_3_3_1_42_2","unstructured":"Janghwan Lee Jiwoong Park Jinseok Kim Yongjik Kim Jungju Oh Jinwook Oh and Jungwook Choi. 2024. AMXFP4: Taming Activation Outliers with Asymmetric Microscaling Floating-Point for 4-bit LLM Inference. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2411.09909 (2024)."},{"key":"e_1_3_3_1_43_2","unstructured":"Jinhao Li Jiaming Xu Shiyao Li Shan Huang Jun Liu Yaoxiu Lian and Guohao Dai. 2023. Fast and Efficient 2-bit LLM Inference on GPU: 2\/4\/16-bit in a Weight Matrix with Asynchronous Dequantization. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2311.16442 (2023)."},{"key":"e_1_3_3_1_44_2","volume-title":"The Efficient Natural Language and Speech Processing Workshop with NeurIPS","author":"Li Shiyao","year":"2023","unstructured":"Shiyao Li, Xuefei Ning, Ke Hong, Tengxuan Liu, Luning Wang, Xiuhong Li, Kai Zhong, Guohao Dai, Huazhong Yang, and Yu Wang. 2023. Llm-mq: Mixed-precision quantization for efficient llm deployment. In The Efficient Natural Language and Speech Processing Workshop with NeurIPS , Vol.\u00a09."},{"key":"e_1_3_3_1_45_2","unstructured":"Shiyao Li Xuefei Ning Luning Wang Tengxuan Liu Xiangsheng Shi Shengen Yan Guohao Dai Huazhong Yang and Yu Wang. 2024. Evaluating quantized large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2402.18158 (2024)."},{"key":"e_1_3_3_1_46_2","doi-asserted-by":"publisher","DOI":"10.1145\/3669940.3707268"},{"key":"e_1_3_3_1_47_2","doi-asserted-by":"crossref","unstructured":"Haokun Lin Haobo Xu Yichen Wu Jingzhi Cui Yingtao Zhang Linzhan Mou Linqi Song Zhenan Sun and Ying Wei. 2024. Duquant: Distributing outliers via dual transformation makes stronger quantized llms. Advances in Neural Information Processing Systems 37 (2024) 87766\u201387800.","DOI":"10.52202\/079017-2786"},{"key":"e_1_3_3_1_48_2","unstructured":"Ji Lin Jiaming Tang Haotian Tang Shang Yang Wei-Ming Chen Wei-Chen Wang Guangxuan Xiao Xingyu Dang Chuang Gan and Song Han. 2024. AWQ: Activation-aware Weight Quantization for On-Device LLM Compression and Acceleration. Proceedings of Machine Learning and Systems 6 (2024) 87\u2013100."},{"key":"e_1_3_3_1_49_2","unstructured":"Aixin Liu Bei Feng Bing Xue Bingxuan Wang Bochao Wu Chengda Lu Chenggang Zhao Chengqi Deng Chenyu Zhang Chong Ruan et\u00a0al. 2024. Deepseek-v3 technical report. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2412.19437 (2024)."},{"key":"e_1_3_3_1_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA57654.2024.00082"},{"key":"e_1_3_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA61900.2025.00075"},{"key":"e_1_3_3_1_52_2","doi-asserted-by":"crossref","unstructured":"Lei Liu Yong Li Chen Ding Hao Yang and Chengyong Wu. 2015. Rethinking memory management in modern operating system: Horizontal vertical or random?IEEE Trans. Comput. 65 6 (2015) 1921\u20131935.","DOI":"10.1109\/TC.2015.2462813"},{"key":"e_1_3_3_1_53_2","doi-asserted-by":"crossref","unstructured":"Lei Liu Shengjie Yang Lu Peng and Xinyu Li. 2019. Hierarchical hybrid memory management in OS for tiered memory systems. IEEE Transactions on Parallel and Distributed Systems 30 10 (2019) 2223\u20132236.","DOI":"10.1109\/TPDS.2019.2908175"},{"key":"e_1_3_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/HPCA61900.2025.00112"},{"key":"e_1_3_3_1_55_2","unstructured":"Yuexiao Ma Huixia Li Xiawu Zheng Feng Ling Xuefeng Xiao Rui Wang Shilei Wen Fei Chao and Rongrong Ji. 2024. Affinequant: Affine transformation quantization for large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2403.12544 (2024)."},{"key":"e_1_3_3_1_56_2","unstructured":"Stephen Merity Caiming Xiong James Bradbury and Richard Socher. 2016. Pointer sentinel mixture models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1609.07843 (2016)."},{"key":"e_1_3_3_1_57_2","unstructured":"Asit Mishra Dusan Stosic and Simon Layton. 2025. Recipes for Pre-training LLMs with MXFP8. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2506.08027 (2025)."},{"key":"e_1_3_3_1_58_2","unstructured":"Zhiwen Mo Lei Wang Jianyu Wei Zhichen Zeng Shijie Cao Lingxiao Ma Naifeng Jing Ting Cao Jilong Xue Fan Yang et\u00a0al. 2024. Lut tensor core: Lookup table enables efficient low-bit llm inference acceleration. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2408.06003 (2024)."},{"key":"e_1_3_3_1_59_2","doi-asserted-by":"crossref","unstructured":"Jaehyun Nam Kyuyoung Kim Seunghyuk Oh Jihoon Tack Jaehyung Kim and Jinwoo Shin. 2024. Optimized feature generation for tabular data via llms with decision tree reasoning. Advances in Neural Information Processing Systems 37 (2024) 92352\u201392380.","DOI":"10.52202\/079017-2932"},{"key":"e_1_3_3_1_60_2","unstructured":"NVIDIA NVIDIA. 2020. NVIDIA A100 tensor core GPU architecture. Volume 1.0: Whitepaper Part 1 2020 (2020) 82."},{"key":"e_1_3_3_1_61_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICECS.2016.7841286"},{"key":"e_1_3_3_1_62_2","unstructured":"Jiayi Pan Chengcan Wang Kaifu Zheng Yangguang Li Zhenyu Wang and Bin Feng. 2023. Smoothquant+: Accurate and efficient 4-bit post-training weightquantization for llm. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2312.03788 (2023)."},{"key":"e_1_3_3_1_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISPASS.2019.00042"},{"key":"e_1_3_3_1_64_2","unstructured":"A Paszke. 2019. Pytorch: An imperative style high-performance deep learning library. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1912.01703 (2019)."},{"key":"e_1_3_3_1_65_2","doi-asserted-by":"crossref","unstructured":"Ram Rangan Naman Turakhia and Alexandre Joly. 2020. Countering load-to-use stalls in the NVIDIA turing GPU. IEEE Micro 40 6 (2020) 59\u201366.","DOI":"10.1109\/MM.2020.3012514"},{"key":"e_1_3_3_1_66_2","unstructured":"Bita\u00a0Darvish Rouhani Ritchie Zhao Ankit More Mathew Hall Alireza Khodamoradi Summer Deng Dhruv Choudhary Marius Cornea Eric Dellinger Kristof Denolf et\u00a0al. 2023. Microscaling data formats for deep learning. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2310.10537 (2023)."},{"key":"e_1_3_3_1_67_2","doi-asserted-by":"crossref","unstructured":"Keisuke Sakaguchi Ronan\u00a0Le Bras Chandra Bhagavatula and Yejin Choi. 2021. Winogrande: An adversarial winograd schema challenge at scale. Commun. ACM 64 9 (2021) 99\u2013106.","DOI":"10.1145\/3474381"},{"key":"e_1_3_3_1_68_2","unstructured":"Wenqi Shao Mengzhao Chen Zhaoyang Zhang Peng Xu Lirui Zhao Zhiqian Li Kaipeng Zhang Peng Gao Yu Qiao and Ping Luo. 2023. Omniquant: Omnidirectionally calibrated quantization for large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2308.13137 (2023)."},{"key":"e_1_3_3_1_69_2","doi-asserted-by":"crossref","unstructured":"Jaehyeong Sim Somin Lee and Lee-Sup Kim. 2019. An energy-efficient deep convolutional neural network inference processor with enhanced output stationary dataflow in 65-nm CMOS. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 28 1 (2019) 87\u2013100.","DOI":"10.1109\/TVLSI.2019.2935251"},{"key":"e_1_3_3_1_70_2","doi-asserted-by":"crossref","unstructured":"Wei Sun Ang Li Tong Geng Sander Stuijk and Henk Corporaal. 2022. Dissecting tensor cores via microbenchmarks: Latency throughput and numeric behaviors. IEEE Transactions on Parallel and Distributed Systems 34 1 (2022) 246\u2013261.","DOI":"10.1109\/TPDS.2022.3217824"},{"key":"e_1_3_3_1_71_2","volume-title":"CACTI 5.1","author":"Thoziyoor Shyamkumar","year":"2008","unstructured":"Shyamkumar Thoziyoor, Naveen Muralimanohar, Jung\u00a0Ho Ahn, and Norman\u00a0P Jouppi. 2008. CACTI 5.1. Technical Report. Technical Report HPL-2008-20, HP Labs."},{"key":"e_1_3_3_1_72_2","doi-asserted-by":"publisher","DOI":"10.1109\/HCS61935.2024.10665247"},{"key":"e_1_3_3_1_73_2","unstructured":"Hugo Touvron Thibaut Lavril Gautier Izacard Xavier Martinet Marie-Anne Lachaux Timoth\u00e9e Lacroix Baptiste Rozi\u00e8re Naman Goyal Eric Hambro Faisal Azhar et\u00a0al. 2023. Llama: Open and efficient foundation language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2302.13971 (2023)."},{"key":"e_1_3_3_1_74_2","unstructured":"A Vaswani. 2017. Attention is all you need. Advances in Neural Information Processing Systems (2017)."},{"key":"e_1_3_3_1_75_2","doi-asserted-by":"crossref","unstructured":"David Wang Brinda Ganesh Nuengwong Tuaycharoen Kathleen Baynes Aamer Jaleel and Bruce Jacob. 2005. Dramsim: a memory system simulator. ACM SIGARCH Computer Architecture News 33 4 (2005) 100\u2013107.","DOI":"10.1145\/1105734.1105748"},{"key":"e_1_3_3_1_76_2","doi-asserted-by":"crossref","unstructured":"Shirley Wu Shiyu Zhao Qian Huang Kexin Huang Michihiro Yasunaga Kaidi Cao Vassilis Ioannidis Karthik Subbian Jure Leskovec and James\u00a0Y Zou. 2024. Avatar: Optimizing llm agents for tool usage via contrastive reasoning. Advances in Neural Information Processing Systems 37 (2024) 25981\u201326010.","DOI":"10.52202\/079017-0817"},{"key":"e_1_3_3_1_77_2","unstructured":"Haojun Xia Zhen Zheng Xiaoxia Wu Shiyang Chen Zhewei Yao Stephen Youn Arash Bakhtiari Michael Wyatt Donglin Zhuang Zhongzhu Zhou et\u00a0al. 2024. Fp6-llm: Efficiently serving large language models through fp6-centric algorithm-system co-design. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2401.14112 (2024)."},{"key":"e_1_3_3_1_78_2","first-page":"38087","volume-title":"International Conference on Machine Learning","author":"Xiao Guangxuan","year":"2023","unstructured":"Guangxuan Xiao, Ji Lin, Mickael Seznec, Hao Wu, Julien Demouth, and Song Han. 2023. Smoothquant: Accurate and efficient post-training quantization for large language models. In International Conference on Machine Learning. PMLR, 38087\u201338099."},{"key":"e_1_3_3_1_79_2","doi-asserted-by":"publisher","DOI":"10.23919\/DATE64628.2025.10993129"},{"key":"e_1_3_3_1_80_2","unstructured":"Zhihang Yuan Yuzhang Shang Yang Zhou Zhen Dong Zhe Zhou Chenhao Xue Bingzhe Wu Zhikai Li Qingyi Gu Yong\u00a0Jae Lee et\u00a0al. 2024. Llm inference unveiled: Survey and roofline model insights. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2402.16363 (2024)."},{"key":"e_1_3_3_1_81_2","doi-asserted-by":"publisher","DOI":"10.1109\/MICRO50266.2020.00071"},{"key":"e_1_3_3_1_82_2","doi-asserted-by":"publisher","DOI":"10.1145\/3470496.3527438"},{"key":"e_1_3_3_1_83_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i21.34385"},{"key":"e_1_3_3_1_84_2","unstructured":"Susan Zhang Stephen Roller Naman Goyal Mikel Artetxe Moya Chen Shuohui Chen Christopher Dewan Mona Diab Xian Li Xi\u00a0Victoria Lin et\u00a0al. 2022. Opt: Open pre-trained transformer language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2205.01068 (2022)."},{"key":"e_1_3_3_1_85_2","doi-asserted-by":"publisher","DOI":"10.1145\/3710848.3710888"},{"key":"e_1_3_3_1_86_2","unstructured":"Wayne\u00a0Xin Zhao Kun Zhou Junyi Li Tianyi Tang Xiaolei Wang Yupeng Hou Yingqian Min Beichen Zhang Junjie Zhang Zican Dong et\u00a0al. 2023. A survey of large language models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2303.18223 1 2 (2023)."},{"key":"e_1_3_3_1_87_2","doi-asserted-by":"publisher","DOI":"10.1145\/3352460.3358269"}],"event":{"name":"MICRO 2025: 58th IEEE\/ACM International Symposium on Microarchitecture","location":"Seoul Korea","acronym":"MICRO 2025","sponsor":["SIGMICRO ACM Special Interest Group on Microarchitectural Research and Processing"]},"container-title":["Proceedings of the 58th IEEE\/ACM International Symposium on Microarchitecture"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3725843.3756113","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,26]],"date-time":"2026-01-26T21:41:53Z","timestamp":1769463713000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3725843.3756113"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,17]]},"references-count":86,"alternative-id":["10.1145\/3725843.3756113","10.1145\/3725843"],"URL":"https:\/\/doi.org\/10.1145\/3725843.3756113","relation":{},"subject":[],"published":{"date-parts":[[2025,10,17]]},"assertion":[{"value":"2025-10-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}