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)

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)

A Switched-Capacitor Integer Compute Unit with Decoupled Storage and Arithmetic for Cloud AI Inference in 5nm CMOS. - Ankur Agrawal; Monodeep Kar; Kyu-Hyoun Kim; Sergey V. Rylov; Jinwook Jung; Seiji Munetoh; Kohji Hosokawa; Xin Zhang 025; Bahman Hekmatshoartabari; Fabio Carta; Martin Cochet; Robert Casatuta; et al. (VLSI Technology and Circuits, 2023)