Kailash Gopalakrishnan

发表

Nanoscale electronic synapses using phase change devices

Geoffrey W. Burr, Dharmendra S. Modha, Bryan L. Jackson, 2013, JETC.

Approximate computing: Challenges and opportunities

Vijayalakshmi Srinivasan, Zehra Sura, Sunil Shukla, 2016, 2016 IEEE International Conference on Rebooting Computing (ICRC).

Exploiting approximate computing for deep learning acceleration

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).

Training Deep Neural Networks with 8-bit Floating Point Numbers

Daniel Brand, Kailash Gopalakrishnan, Jungwook Choi, 2018, NeurIPS.

Ultra-Low Precision 4-bit Training of Deep Neural Networks

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2020, NeurIPS.

AdaComp : Adaptive Residual Gradient Compression for Data-Parallel Distributed Training pdf

Wei Zhang, Daniel Brand, Ankur Agrawal, 2017, AAAI.

Controlling linewidth roughness in step and flash imprint lithography

Hideo Kobayashi, Naoya Hayashi, Mary Beth Rothwell, 2008, European Mask and Lithography Conference.

The inner workings of phase change memory: Lessons from prototype PCM devices

Kailash Gopalakrishnan, Alvaro Padilla, John Karidis, 2010, 2010 IEEE Globecom Workshops.

Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN) pdf

Swagath Venkataramani, Zhuo Wang, Vijayalakshmi Srinivasan, 2018, ArXiv.

Exploring the Design Space for Crossbar Arrays Built With Mixed-Ionic-Electronic-Conduction (MIEC) Access Devices

Pritish Narayanan, Kumar Virwani, Kailash Gopalakrishnan, 2015, IEEE Journal of the Electron Devices Society.

Accurate and Efficient 2-bit Quantized Neural Networks

Swagath Venkataramani, Vijayalakshmi Srinivasan, Zhuo Wang, 2019, MLSys.

Taming the beast: Programming Peta-FLOP class Deep Learning Systems

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2018, ISLPED.

Learning Machines Implemented on Non-Deterministic Hardware pdf

Vikas Sindhwani, Kailash Gopalakrishnan, Suyog Gupta, 2014, ArXiv.

PACT: Parameterized Clipping Activation for Quantized Neural Networks pdf

Swagath Venkataramani, Zhuo Wang, Vijayalakshmi Srinivasan, 2018, ArXiv.

Across the Stack Opportunities for Deep Learning Acceleration

Joel Silberman, Swagath Venkataramani, Matthew M. Ziegler, 2018, ISLPED.

BiScaled-DNN: Quantizing Long-tailed Datastructures with Two Scale Factors for Deep Neural Networks

Swagath Venkataramani, Vijayalakshmi Srinivasan, Shubham Jain, 2019, 2019 56th ACM/IEEE Design Automation Conference (DAC).

Efficient AI System Design With Cross-Layer Approximate Computing

Swagath Venkataramani, Xiao Sun, Mingu Kang, 2020, Proceedings of the IEEE.

Invited: Accelerator design for deep learning training

Swagath Venkataramani, Sunil Shukla, Kailash Gopalakrishnan, 2017, 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC).

Overview of candidate device technologies for storage-class memory

Kailash Gopalakrishnan, Geoffrey W. Burr, Bülent N. Kurdi, 2008, IBM J. Res. Dev..

A Scalable Multi-TeraOPS Core for AI Training and Inference

Swagath Venkataramani, Vijayalakshmi Srinivasan, George Gristede, 2018, IEEE Solid-State Circuits Letters.

POSTER: Design Space Exploration for Performance Optimization of Deep Neural Networks on Shared Memory Accelerators

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2017, 2017 26th International Conference on Parallel Architectures and Compilation Techniques (PACT).

A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel

Wei Wang, Swagath Venkataramani, Hiroshi Inoue, 2019, 2019 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS).

Deep Learning with Limited Numerical Precision

Pritish Narayanan, Ankur Agrawal, Kailash Gopalakrishnan, 2015, ICML.

Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2019, NeurIPS.

True Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization

Charbel Sakr, Naresh R. Shanbhag, Zhuo Wang, 2018, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Energy-Efficient Simultaneous Localization and Mapping via Compounded Approximate Computing

Kailash Gopalakrishnan, Jungwook Choi, Jinwook Oh, 2016, 2016 IEEE International Workshop on Signal Processing Systems (SiPS).

DLFloat: A 16-b Floating Point Format Designed for Deep Learning Training and Inference

Kailash Gopalakrishnan, Jungwook Choi, Ankur Agrawal, 2019, 2019 IEEE 26th Symposium on Computer Arithmetic (ARITH).

ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training

Swagath Venkataramani, Vijayalakshmi Srinivasan, Songtao Lu, 2021, NeurIPS.

A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference

Joel Silberman, Swagath Venkataramani, Matthew M. Ziegler, 2018, 2018 IEEE Symposium on VLSI Circuits.

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training pdf

Yue Wang, Yang Zhao, Chaojian Li, 2020, NeurIPS.

Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks pdf

Charbel Sakr, Naresh R. Shanbhag, Ankur Agrawal, 2019, ICLR.

Memory and Interconnect Optimizations for Peta-Scale Deep Learning Systems

Swagath Venkataramani, Philip Heidelberger, Vijayalakshmi Srinivasan, 2019, 2019 IEEE 26th International Conference on High Performance Computing, Data, and Analytics (HiPC).

A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference

Swagath Venkataramani, Xiao Sun, Mingu Kang, 2020, 2020 IEEE Symposium on VLSI Circuits.

Performance-driven Programming of Multi-TFLOP Deep Learning Accelerators*

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2019, 2019 IEEE International Symposium on Workload Characterization (IISWC).

Accelerator Design for Deep Learning Training: Extended Abstract: Invited

Wei Zhang, Swagath Venkataramani, Vijayalakshmi Srinivasan, 2017, DAC.

4-bit Quantization of LSTM-based Speech Recognition Models pdf

Brian Kingsbury, George Saon, Swagath Venkataramani, 2021, Interspeech.

Efficient Management of Scratch-Pad Memories in Deep Learning Accelerators

Swagath Venkataramani, Vijayalakshmi Srinivasan, Kailash Gopalakrishnan, 2021, 2021 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

RaPiD: AI Accelerator for Ultra-low Precision Training and Inference

Joel Silberman, Swagath Venkataramani, Matthew M. Ziegler, 2021, 2021 ACM/IEEE 48th Annual International Symposium on Computer Architecture (ISCA).

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

Joel Silberman, Swagath Venkataramani, Vijayalakshmi Srinivasan, 2022, IEEE Journal of Solid-State Circuits.

A 7nm 4-Core AI Chip with 25.6TFLOPS Hybrid FP8 Training, 102.4TOPS INT4 Inference and Workload-Aware Throttling

Joel Silberman, Swagath Venkataramani, Mingu Kang, 2021, 2021 IEEE International Solid- State Circuits Conference (ISSCC).

Position paper submitted to WACAS 2014 1 Revisiting Stochastic Computation : Approximate Estimation of Machine Learning Kernels

Kailash Gopalakrishnan, Suyog Gupta, 2014 .

On the Origin of Steep $I$ – $V$ Nonlinearity in Mixed-Ionic-Electronic-Conduction-Based Access Devices

Pritish Narayanan, Kumar Virwani, Kailash Gopalakrishnan, 2015, IEEE Transactions on Electron Devices.

MIEC (mixed-ionic-electronic-conduction)-based access devices for non-volatile crossbar memory arrays

Pritish Narayanan, Kumar Virwani, Philip M. Rice, 2014 .

Voltage polarity effects in Ge2Sb2Te5-based phase change memory devices

Kumar Virwani, Philip M. Rice, Kailash Gopalakrishnan, 2011 .