Proceedings - International Symposium on Computer Architecture/Proceedings 2023 — accepted papers
Proceedings - International Symposium on Computer Architecture/Proceedings 2023 is the 2023 edition of the Proceedings - International Symposium on Computer Architecture/Proceedings. OpenAlex lists 84 papers of it; our library holds 1 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.
Most cited papers
- TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for EmbeddingsNorman Paul Jouppi, George Kurian, Sheng Li et al.508 citations · our summary
- OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization ↗Cong Guo, Jiaming Tang, Weiming Hu et al.152 citations
- FACT: FFN-Attention Co-optimized Transformer Architecture with Eager Correlation Prediction ↗Yubin Qin, Yang Wang, Dazheng Deng et al.116 citations
- SHARP: A Short-Word Hierarchical Accelerator for Robust and Practical Fully Homomorphic Encryption ↗Jongmin Kim, Sangpyo Kim, Jaewan Choi et al.112 citations
- Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction ↗Sixu Li, Chaojian Li, Wenbo Zhu et al.79 citations
- RowPress: Amplifying Read Disturbance in Modern DRAM Chips ↗Haocong Luo, Ataberk Olgun, A. Giray Yağlıkçı et al.74 citations
- Inter-layer Scheduling Space Definition and Exploration for Tiled Accelerators ↗Jingwei Cai, Yuchen Wei, Zuotong Wu et al.66 citations
- With Shared Microexponents, A Little Shifting Goes a Long Way ↗Bita Darvish Rouhani, Ritchie Zhao, Venmugil Elango et al.54 citations
- MTIA: First Generation Silicon Targeting Meta's Recommendation Systems ↗Amin Firoozshahian, Joel Coburn, Roman Levenstein et al.53 citations
- Understanding and Mitigating Hardware Failures in Deep Learning Training Systems ↗Yi He, Mike Hutton, Steven Chan et al.53 citations
All accepted papers (84)
- TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for EmbeddingsNorman Paul Jouppi, George Kurian, Sheng Li et al.508 citations · our summary
- OliVe: Accelerating Large Language Models via Hardware-friendly Outlier-Victim Pair Quantization ↗Cong Guo, Jiaming Tang, Weiming Hu et al.152 citations
- FACT: FFN-Attention Co-optimized Transformer Architecture with Eager Correlation Prediction ↗Yubin Qin, Yang Wang, Dazheng Deng et al.116 citations
- SHARP: A Short-Word Hierarchical Accelerator for Robust and Practical Fully Homomorphic Encryption ↗Jongmin Kim, Sangpyo Kim, Jaewan Choi et al.112 citations
- Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction ↗Sixu Li, Chaojian Li, Wenbo Zhu et al.79 citations
- RowPress: Amplifying Read Disturbance in Modern DRAM Chips ↗Haocong Luo, Ataberk Olgun, A. Giray Yağlıkçı et al.74 citations
- Inter-layer Scheduling Space Definition and Exploration for Tiled Accelerators ↗Jingwei Cai, Yuchen Wei, Zuotong Wu et al.66 citations
- With Shared Microexponents, A Little Shifting Goes a Long Way ↗Bita Darvish Rouhani, Ritchie Zhao, Venmugil Elango et al.54 citations
- MTIA: First Generation Silicon Targeting Meta's Recommendation Systems ↗Amin Firoozshahian, Joel Coburn, Roman Levenstein et al.53 citations
- Understanding and Mitigating Hardware Failures in Deep Learning Training Systems ↗Yi He, Mike Hutton, Steven Chan et al.53 citations
- NeuRex: A Case for Neural Rendering Acceleration ↗Junseo Lee, Kwanseok Choi, Jungi Lee et al.49 citations
- Astrea: Accurate Quantum Error-Decoding via Practical Minimum-Weight Perfect-Matching ↗Suhas Vittal, Poulami Das, Moinuddin K. Qureshi47 citations
- RAELLA: Reforming the Arithmetic for Efficient, Low-Resolution, and Low-Loss Analog PIM: No Retraining Required! ↗Tanner Andrulis, Joel Emer, Vivienne Sze46 citations
- Spy in the GPU-box: Covert and Side Channel Attacks on Multi-GPU Systems ↗Sankha Baran Dutta, Hoda Naghibijouybari, Arjun K. Gupta et al.42 citations
- MXFaaS: Resource Sharing in Serverless Environments for Parallelism and Efficiency ↗Jovan Stojkovic, Tianyin Xu, Hubertus Franke et al.39 citations
- MetaNMP: Leveraging Cartesian-Like Product to Accelerate HGNNs with Near-Memory Processing ↗Dan Chen, Haiheng He, Hai Jin et al.36 citations
- Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design ↗Yonggan Fu, Zhifan Ye, Jiayi Yuan et al.34 citations
- Accelerating Personalized Recommendation with Cross-level Near-Memory Processing ↗Haifeng Liu, Long Zheng, Yu Huang et al.31 citations
- Hardware Acceleration of Neural Graphics ↗Muhammad Husnain Mubarik, Ramakrishna Kanungo, Tobias Zirr et al.30 citations
- MapZero: Mapping for Coarse-grained Reconfigurable Architectures with Reinforcement Learning and Monte-Carlo Tree Search ↗Xiangyu Kong, Yi Cheng Huang, Jianfeng Zhu et al.30 citations
- Profiling Hyperscale Big Data Processing ↗Abraham Gonzalez, Aasheesh Kolli, Samira Khan et al.30 citations
- Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures ↗Satvik Maurya, Chaithanya Naik Mude, William D. Oliver et al.30 citations
- Venice: Improving Solid-State Drive Parallelism at Low Cost via Conflict-Free Accesses ↗Rakesh Nadig, Mohammad Sadrosadati, Haiyu Mao et al.29 citations
- EdgePC: Efficient Deep Learning Analytics for Point Clouds on Edge Devices ↗Ziyu Ying, Sandeepa Bhuyan, Yan Kang et al.28 citations
- LeCA: In-Sensor Learned Compressive Acquisition for Efficient Machine Vision on the Edge ↗Tianrui Ma, Adith Boloor, Xiangxing Yang et al.27 citations
Papers with our summary in our library (1)
- TPU v4: An Optically Reconfigurable Supercomputer for Machine Learning with Hardware Support for EmbeddingsProceedings - International Symposium on Computer Architecture/ProceedingsNorman P. Jouppi; George Kurian; Sheng Li; Peter Ma; Rahul Nagarajan; Lifeng Nai; Nishant Patil; Suvinay Subramanian; Andy Swing; Brian Towles; Cliff Young; Xiang Zhou; Zongwei Zhou;, David Patterson2023 · 615 citations
Top topics
Fields
Top authors
Top institutions
🏢GoogleMountain View ·
United States1 work here🎓University of California, BerkeleyBerkeley ·
United States1 work here
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