Chen, Chia-Yu

8 publications

ICML 2024 Reshape and Adapt for Output Quantization (RAOQ): Quantization-Aware Training for In-Memory Computing Systems Bonan Zhang, Chia-Yu Chen, Naveen Verma

NeurIPS 2022 Deep Compression of Pre-Trained Transformer Models Naigang Wang, Chi-Chun Liu, Swagath Venkataramani, Sanchari Sen, Chia-Yu Chen, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Leland Chang

NeurIPS 2020 ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training Chia-Yu Chen, Jiamin Ni, Songtao Lu, Xiaodong Cui, Pin-Yu Chen, Xiao Sun, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Wei Zhang, Kailash Gopalakrishnan

NeurIPS 2020 Ultra-Low Precision 4-Bit Training of Deep Neural Networks Xiao Sun, Naigang Wang, Chia-Yu Chen, Jiamin Ni, Ankur Agrawal, Xiaodong Cui, Swagath Venkataramani, Kaoutar El Maghraoui, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan

ICLR 2019 Accumulation Bit-Width Scaling for Ultra-Low Precision Training of Deep Networks Charbel Sakr, Naigang Wang, Chia-Yu Chen, Jungwook Choi, Ankur Agrawal, Naresh Shanbhag, Kailash Gopalakrishnan

NeurIPS 2019 Hybrid 8-Bit Floating Point (HFP8) Training and Inference for Deep Neural Networks Xiao Sun, Jungwook Choi, Chia-Yu Chen, Naigang Wang, Swagath Venkataramani, Vijayalakshmi Srinivasan, Xiaodong Cui, Wei Zhang, Kailash Gopalakrishnan

AAAI 2018 AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training Chia-Yu Chen, Jungwook Choi, Daniel Brand, Ankur Agrawal, Wei Zhang, Kailash Gopalakrishnan

NeurIPS 2018 Training Deep Neural Networks with 8-Bit Floating Point Numbers Naigang Wang, Jungwook Choi, Daniel Brand, Chia-Yu Chen, Kailash Gopalakrishnan