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Author

Kailash Gopalakrishnan

Observed names: Gopalakrishnan, Kailash, K. Gopalakrishnan
Observed institutions: University of Nebraska–Lincoln, IBM (United States), East Carolina University, IBM Research - Almaden, IBM Research - Thomas J. Watson Research Center, Comisión Chilena de Energía Nuclear, Stanford University
ORCID: 0000-0002-8952-0875
Observed ORCID: 0000-0002-8952-0875
OpenAlex ID: A5101429456
Deep Learning with Limited Numerical Precision
2015 · Gupta, Suyog, Agrawal, Ankur, et al. · arXiv (Cornell University) · 1,445
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PACT: Parameterized Clipping Activation for Quantized Neural Networks
2018 · Choi, Jungwook, Wang, Zhuo, et al. · arXiv (Cornell University) · 1,081
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Deep Learning with Limited Numerical Precision
2015 · Suyog Gupta, Ankur Agrawal, et al. · 757
Activation and diffusion studies of ion-implanted p and n dopants in germanium
2003 · Chi On Chui, Kailash Gopalakrishnan, et al. · Applied Physics Letters · 280
AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training
2018 · Chia-Yu Chen, Jungwook Choi, et al. · Proceedings of the AAAI Conference on Artificial Intelligence · 176
Nanoscale electronic synapses using phase change devices
2013 · Bryan L. Jackson, Bipin Rajendran, et al. · ACM Journal on Emerging Technologies in Computing Systems · 168
Specifications of Nanoscale Devices and Circuits for Neuromorphic Computational Systems
2012 · Bipin Rajendran, Yong Liu, et al. · IEEE Transactions on Electron Devices · 158
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks
2019 · Xiao Sun, Jungwook Choi, et al. · neural information processing systems · 125
9.1 A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling
2021 · Ankur Agrawal, Sae Kyu Lee, et al. · Digest of technical papers/Digest of technical papers - IEEE International Solid-State Circuits Conference · 107
Approximate computing: Challenges and opportunities
2016 · Ankur Agrawal, Zehra Sura, et al. · IEEE International Conference on Rebooting Computing (ICRC) · 101
DLFloat: A 16-b Floating Point Format Designed for Deep Learning Training and Inference
2019 · Ankur Agrawal, Silvia M. Mueller, et al. · IEEE Symposium on Computer Arithmetic (ARITH) · 94
RaPiD: AI Accelerator for Ultra-low Precision Training and Inference
2021 · Swagath Venkataramani, Vijayalakshmi Srinivasan, et al. · Proceedings - International Symposium on Computer Architecture/Proceedings · 84
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)
2018 · Choi, Jungwook, Chuang, Pierce I-Jen, et al. · arXiv (Cornell University) · 81
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Voltage polarity effects in Ge2Sb2Te5-based phase change memory devices
2011 · Alvaro Padilla, Geoffrey W. Burr, et al. · Journal of Applied Physics · 79
Efficient AI System Design With Cross-Layer Approximate Computing
2020 · Swagath Venkataramani, Xiao Sun, et al. · Proceedings of the IEEE · 74
Sub-30nm scaling and high-speed operation of fully-confined Access-Devices for 3D crosspoint memory based on mixed-ionic-electronic-conduction (MIEC) materials
2012 · K. Virwani, G. W. Burr, et al. · Technical digest - International Electron Devices Meeting/Technical digest · 71
MIEC (mixed-ionic-electronic-conduction)-based access devices for non-volatile crossbar memory arrays
2014 · Rohit S Shenoy, Geoffrey W Burr, et al. · Semiconductor Science and Technology · 63
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Protocols for watermark verification
2001 · K. Gopalakrishnan, N. Memon, et al. · IEEE Multimedia · 55
ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training
2021 · Chen, Chia-Yu, Ni, Jiamin, et al. · arXiv (Cornell University) · 53
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A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference
2020 · Jinwook Oh, Sae Kyu Lee, et al. · Symposium on VLSI Circuits · 41
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
2021 · Sae Kyu Lee, Ankur Agrawal, et al. · IEEE Journal of Solid-State Circuits · 39
A Scalable Multi-TeraOPS Core for AI Training and Inference
2018 · Sunil Shukla, Bruce Fleischer, et al. · IEEE Solid-State Circuits Letters · 39
BiScaled-DNN
2019 · Shubham Jain, Swagath Venkataramani, et al. · ACM/IEEE Design Automation Conference (DAC) · 36
FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
2020 · Fu, Yonggan, You, Haoran, et al. · arXiv (Cornell University) · 32
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Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks
2019 · Sakr, Charbel, Wang, Naigang, et al. · arXiv (Cornell University) · 30
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Works count: 50
Citation count: 5,451
H-index: 26
I10-index: 31
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