Adaptive refreshing and read voltage control scheme for FeDRAM. - Ismail Bayram; Enes Eken; Xue Wang; Xiao Sun 013; T. P. Ma; Yiran Chen 001 (ISCAS, 2016)

DLFloat: A 16-b Floating Point Format Designed for Deep Learning Training and Inference. - Ankur Agrawal; Bruce M. Fleischer; Silvia M. Mueller; Xiao Sun 013; Naigang Wang; Jungwook Choi; Kailash Gopalakrishnan (ARITH, 2019)

Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks. - Xiao Sun 013; Jungwook Choi; Chia-Yu Chen; Naigang Wang; Swagath Venkataramani; Vijayalakshmi Srinivasan; Xiaodong Cui; Wei Zhang 022; Kailash Gopalakrishnan (NeurIPS, 2019)

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training. - Chia-Yu Chen; Jiamin Ni; Songtao Lu; Xiaodong Cui; Pin-Yu Chen; Xiao Sun 013; Naigang Wang; Swagath Venkataramani; Vijayalakshmi Srinivasan; Wei Zhang 022; Kailash Gopalakrishnan (NeurIPS, 2020)

Ultra-Low Precision 4-bit Training of Deep Neural Networks. - Xiao Sun 013; Naigang Wang; Chia-Yu Chen; Jiamin Ni; Ankur Agrawal; Xiaodong Cui; Swagath Venkataramani; Kaoutar El Maghraoui; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan (NeurIPS, 2020)

A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference. - Jinwook Oh; Sae Kyu Lee; Mingu Kang; Matthew M. Ziegler; Joel Silberman; Ankur Agrawal; Swagath Venkataramani; Bruce M. Fleischer; Michael Guillorn; Jungwook Choi; Wei Wang 333; Silvia M. Mueller; et al. (VLSI Circuits, 2020)

Efficient AI System Design With Cross-Layer Approximate Computing. - Swagath Venkataramani; Xiao Sun 013; Naigang Wang; Chia-Yu Chen; Jungwook Choi; Mingu Kang; Ankur Agarwal; Jinwook Oh; Shubham Jain; Tina Babinsky; Nianzheng Cao; Thomas W. Fox; et al. (Proc. IEEE, 2020)

4-Bit Quantization of LSTM-Based Speech Recognition Models. - Andrea Fasoli; Chia-Yu Chen; Mauricio J. Serrano; Xiao Sun 013; Naigang Wang; Swagath Venkataramani; George Saon; Xiaodong Cui; Brian Kingsbury; Wei Zhang 022; Zoltán Tüske; Kailash Gopalakrishnan (Interspeech, 2021)

RaPiD: AI Accelerator for Ultra-low Precision Training and Inference. - Swagath Venkataramani; Vijayalakshmi Srinivasan; Wei Wang 333; Sanchari Sen; Jintao Zhang; Ankur Agrawal; Monodeep Kar; Shubham Jain; Alberto Mannari; Hoang Tran; Yulong Li; Eri Ogawa; et al. (ISCA, 2021)

A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling. - Ankur Agrawal; Sae Kyu Lee; Joel Silberman; Matthew M. Ziegler; Mingu Kang; Swagath Venkataramani; Nianzheng Cao; Bruce M. Fleischer; Michael Guillorn; Matt Cohen; Silvia M. Mueller; Jinwook Oh; et al. (ISSCC, 2021)

A 7-nm Four-Core Mixed-Precision AI Chip With 26.2-TFLOPS Hybrid-FP8 Training, 104.9-TOPS INT4 Inference, and Workload-Aware Throttling. - Sae Kyu Lee; Ankur Agrawal; Joel Silberman; Matthew M. Ziegler; Mingu Kang; Swagath Venkataramani; Nianzheng Cao; Bruce M. Fleischer; Michael Guillorn; Matthew Cohen; Silvia M. Mueller; Jinwook Oh; et al. (IEEE J. Solid State Circuits, 2022)

Revisiting Neural Retrieval on Accelerators. - Jiaqi Zhai; Zhaojie Gong; Yueming Wang; Xiao Sun 013; Zheng Yan 007; Fu Li; Xing Liu (KDD, 2023)