Jiecao Yu

15 papers A* 3C 1Misc 1Journal 9
YearRankTypeTitle / Venue / Authors
2026 J jnl
Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction.
CoRR
Jatin Chhugani, Geonhwa Jeong, Bor-Yiing Su, Yunjie Pan, Hanmei Yang, Aayush Ankit, Jiecao Yu, Summer Deng, Yunqing Chen, Nadathur Satish, Changkyu Kim
2025 J jnl
Accelerating Transformer Inference and Training with 2:4 Activation Sparsity.
CoRR
Daniel Haziza, Timothy Chou, Dhruv Choudhary, Luca Wehrstedt, Francisco Massa, Jiecao Yu, Geonhwa Jeong, Supriya Rao, Patrick Labatut, Jesse Cai
2025 J jnl
Fast and Simplex: 2-Simplicial Attention in Triton.
CoRR
Aurko Roy, Timothy Chou, Sai Surya Duvvuri, Sijia Chen, Jiecao Yu, Xiaodong Wang, Manzil Zaheer, Rohan Anil
2025 A* conf
Scaling Llama 3 Training with Efficient Parallelism Strategies.
ISCA
Weiwei Chu, Xinfeng Xie, Jiecao Yu, Jie Wang, Amar Phanishayee, Chunqiang Tang, Yuchen Hao, Jianyu Huang, Mustafa Ozdal, Jun Wang, Vedanuj Goswami, Naman Goyal, Abhishek Kadian, Andrew Gu, Chris Cai, Feng Tian, Xiaodong Wang, Min Si, Pavan Balaji, Ching-Hsiang Chu, Jongsoo Park
2023 J jnl
BitSET: Bit-Serial Early Termination for Computation Reduction in Convolutional Neural Networks.
ACM Trans. Embed. Comput. Syst.
Yunjie Pan, Jiecao Yu, Andrew Lukefahr, Reetuparna Das, Scott A. Mahlke
2021 C conf
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems.
ICMLA
Xiaocong Du, Bhargav Bhushanam, Jiecao Yu, Dhruv Choudhary, Tianxiang Gao, Sherman Wong, Louis Feng, Jongsoo Park, Yu Cao, Arun Kejariwal
2021 J jnl
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems.
CoRR
Xiaocong Du, Bhargav Bhushanam, Jiecao Yu, Dhruv Choudhary, Tianxiang Gao, Sherman Wong, Louis Feng, Jongsoo Park, Yu Cao, Arun Kejariwal
2021 Misc conf
Compute-Capable Block RAMs for Efficient Deep Learning Acceleration on FPGAs.
FCCM
Xiaowei Wang, Vidushi Goyal, Jiecao Yu, Valeria Bertacco, Andrew Boutros, Eriko Nurvitadhi, Charles Augustine, Ravi R. Iyer, Reetuparna Das
2021 J jnl
First-Generation Inference Accelerator Deployment at Facebook.
CoRR
Michael J. Anderson, Benny Chen, Stephen Chen, Summer Deng, Jordan Fix, Michael Gschwind, Aravind Kalaiah, Changkyu Kim, Jaewon Lee, Jason Liang, Haixin Liu, Yinghai Lu, Jack Montgomery, Arun Moorthy, Nadathur Satish, Sam Naghshineh, Avinash Nayak, Jongsoo Park, Chris Petersen, Martin Schatz, Narayanan Sundaram, Bangsheng Tang, Peter Tang, Amy Yang, Jiecao Yu, Hector Yuen, Ying Zhang, Aravind Anbudurai, Vandana Balan, Harsha Bojja, Joe Boyd, Matthew Breitbach, Claudio Caldato, Anna Calvo, Garret Catron, Sneh Chandwani, Panos Christeas, Brad Cottel, Brian Coutinho, Arun Dalli, Abhishek Dhanotia, Oniel Duncan, Roman Dzhabarov, Simon Elmir, Chunli Fu, Wenyin Fu, Michael Fulthorp, Adi Gangidi, Nick Gibson, Sean Gordon, Beatriz Padilla Hernandez, Daniel Ho, Yu-Cheng Huang, Olof Johansson, Shishir Juluri, et al.
2020 J jnl
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data.
CoRR
Mao Ye, Dhruv Choudhary, Jiecao Yu, Ellie Wen, ZeLiang Chen, Jiyan Yang, Jongsoo Park, Qiang Liu, Arun Kejariwal
2019 A* conf
Bit Prudent In-Cache Acceleration of Deep Convolutional Neural Networks.
HPCA
Xiaowei Wang, Jiecao Yu, Charles Augustine, Ravi R. Iyer, Reetuparna Das
2019 — —
Efficient Deep Neural Network Computation on Processors.
Jiecao Yu
2019 J jnl
Spatial-Winograd Pruning Enabling Sparse Winograd Convolution.
CoRR
Jiecao Yu, Jongsoo Park, Maxim Naumov
2019 J jnl
TF-Net: Deploying Sub-Byte Deep Neural Networks on Microcontrollers.
ACM Trans. Embed. Comput. Syst.
Jiecao Yu, Andrew Lukefahr, Reetuparna Das, Scott A. Mahlke
2017 A* conf
Scalpel: Customizing DNN Pruning to the Underlying Hardware Parallelism.
ISCA
Jiecao Yu, Andrew Lukefahr, David J. Palframan, Ganesh S. Dasika, Reetuparna Das, Scott A. Mahlke