Overview of candidate device technologies for storage-class memory. - Geoffrey W. Burr; Bülent N. Kurdi; J. Campbell Scott; Chung Hon Lam; Kailash Gopalakrishnan; Rohit S. Shenoy (IBM J. Res. Dev., 2008)

Nanoscale electronic synapses using phase change devices. - Bryan L. Jackson; Bipin Rajendran; Gregory S. Corrado; Matthew J. Breitwisch; Geoffrey W. Burr; Roger Cheek; Kailash Gopalakrishnan; Simone Raoux; Charles T. Rettner; Alvaro Padilla; Alejandro G. Schrott; Rohit S. Shenoy; et al. (ACM J. Emerg. Technol. Comput. Syst., 2013)

Learning Machines Implemented on Non-Deterministic Hardware. - Suyog Gupta; Vikas Sindhwani; Kailash Gopalakrishnan (CoRR, 2014)

Deep Learning with Limited Numerical Precision. - Suyog Gupta; Ankur Agrawal; Kailash Gopalakrishnan; Pritish Narayanan (ICML, 2015)

Approximate computing: Challenges and opportunities. - Ankur Agrawal; Jungwook Choi; Kailash Gopalakrishnan; Suyog Gupta; Ravi Nair; Jinwook Oh; Daniel A. Prener; Sunil Shukla; Vijayalakshmi Srinivasan; Zehra Sura (ICRC, 2016)

Energy-Efficient Simultaneous Localization and Mapping via Compounded Approximate Computing. - Jinwook Oh; Jungwook Choi; Guilherme C. Januario; Kailash Gopalakrishnan (SiPS, 2016)

POSTER: Design Space Exploration for Performance Optimization of Deep Neural Networks on Shared Memory Accelerators. - Swagath Venkataramani; Jungwook Choi; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan; Leland Chang (PACT, 2017)

Accelerator Design for Deep Learning Training: Extended Abstract: Invited. - Ankur Agrawal; Chia-Yu Chen; Jungwook Choi; Kailash Gopalakrishnan; Jinwook Oh; Sunil Shukla; Viji Srinivasan; Swagath Venkataramani; Wei Zhang 022 (DAC, 2017)

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

Exploiting approximate computing for deep learning acceleration. - Chia-Yu Chen; Jungwook Choi; Kailash Gopalakrishnan; Viji Srinivasan; Swagath Venkataramani (DATE, 2018)

True Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization. - Charbel Sakr; Jungwook Choi; Zhuo Wang; Kailash Gopalakrishnan; Naresh R. Shanbhag (ICASSP, 2018)

Across the Stack Opportunities for Deep Learning Acceleration. - Vijayalakshmi Srinivasan; Bruce M. Fleischer; Sunil Shukla; Matthew M. Ziegler; Joel Silberman; Jinwook Oh; Jungwook Choi; Silvia M. Mueller; Ankur Agrawal; Tina Babinsky; Nianzheng Cao; Chia-Yu Chen; et al. (ISLPED, 2018)

Taming the beast: Programming Peta-FLOP class Deep Learning Systems. - Swagath Venkataramani; Vijayalakshmi Srinivasan; Jungwook Choi; Kailash Gopalakrishnan; Leland Chang (ISLPED, 2018)

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

A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference. - Bruce M. Fleischer; Sunil Shukla; Matthew M. Ziegler; Joel Silberman; Jinwook Oh; Vijayalakshmi Srinivasan; Jungwook Choi; Silvia M. Mueller; Ankur Agrawal; Tina Babinsky; Nianzheng Cao; Chia-Yu Chen; et al. (VLSI Circuits, 2018)

PACT: Parameterized Clipping Activation for Quantized Neural Networks. - Jungwook Choi; Zhuo Wang; Swagath Venkataramani; Pierce I-Jen Chuang; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan (CoRR, 2018)

Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN). - Jungwook Choi; Pierce I-Jen Chuang; Zhuo Wang; Swagath Venkataramani; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan (CoRR, 2018)

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)

A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel. - Eri Ogawa; Kazuaki Ishizaki; Hiroshi Inoue; Swagath Venkataramani; Jungwook Choi; Wei Wang 333; Vijayalakshmi Srinivasan; Moriyoshi Ohara; Kailash Gopalakrishnan (COOL CHIPS, 2019)

BiScaled-DNN: Quantizing Long-tailed Datastructures with Two Scale Factors for Deep Neural Networks. - Shubham Jain; Swagath Venkataramani; Vijayalakshmi Srinivasan; Jungwook Choi; Kailash Gopalakrishnan; Leland Chang (DAC, 2019)

Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems. - Swagath Venkataramani; Vijayalakshmi Srinivasan; Jungwook Choi; Philip Heidelberger; Leland Chang; Kailash Gopalakrishnan (HiPC, 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 R. Shanbhag; Kailash Gopalakrishnan (ICLR (Poster), 2019)

Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators. - Swagath Venkataramani; Jungwook Choi; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan; Leland Chang (IISWC, 2019)

Accurate and Efficient 2-bit Quantized Neural Networks. - Jungwook Choi; Swagath Venkataramani; Vijayalakshmi Srinivasan; Kailash Gopalakrishnan; Zhuo Wang; Pierce Chuang (SysML, 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)

DeepTools: Compiler and Execution Runtime Extensions for RaPiD AI Accelerator. - Swagath Venkataramani; Jungwook Choi; Vijayalakshmi Srinivasan; Wei Wang 333; Jintao Zhang; Marcel Schaal; Mauricio J. Serrano; Kazuaki Ishizaki; Hiroshi Inoue; Eri Ogawa; Moriyoshi Ohara; Leland Chang; et al. (IEEE Micro, 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)

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training. - Yonggan Fu; Haoran You; Yang Zhao 013; Yue Wang 036; Chaojian Li; Kailash Gopalakrishnan; Zhangyang Wang; Yingyan Lin (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)

Efficient Management of Scratch-Pad Memories in Deep Learning Accelerators. - Subhankar Pal; Swagath Venkataramani; Viji Srinivasan; Kailash Gopalakrishnan (ISPASS, 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)

All at Once Network Quantization via Collaborative Knowledge Transfer. - Ximeng Sun; Rameswar Panda; Chun-Fu Chen 001; Naigang Wang; Bowen Pan; Kailash Gopalakrishnan; Aude Oliva; Rogério Feris; Kate Saenko (CoRR, 2021)

Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization. - Andrea Fasoli; Chia-Yu Chen; Mauricio J. Serrano; Swagath Venkataramani; George Saon; Xiaodong Cui; Brian Kingsbury; Kailash Gopalakrishnan (INTERSPEECH, 2022)

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)

OnSRAM: Efficient Inter-Node On-Chip Scratchpad Management in Deep Learning Accelerators. - Subhankar Pal; Swagath Venkataramani; Viji Srinivasan; Kailash Gopalakrishnan (ACM Trans. Embed. Comput. Syst., 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)