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

C. Sakr, N. Wang, C. Chen, J. Choi, A. Agrawal, N. Shanbhag, and K. Gopalakrishnan. ICLR (Poster), OpenReview.net, (2019)

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Saiprasath Gopalakrishnan

Selvi Gopalakrishnan

Gopalakrishnan Venkiteswaran

Vijay Gopalakrishnan

Kailash Mogare

Other publications of authors with the same name

PACT: Parameterized Clipping Activation for Quantized Neural Networks.J. Choi, Z. Wang, S. Venkataramani, P. Chuang, V. Srinivasan, and K. Gopalakrishnan. CoRR, (2018)Exploiting approximate computing for deep learning acceleration.C. Chen, J. Choi, K. Gopalakrishnan, V. Srinivasan, and S. Venkataramani. DATE, page 821-826. IEEE, (2018)Ultra-Low Precision 4-bit Training of Deep Neural Networks.X. Sun, N. Wang, C. Chen, J. Ni, A. Agrawal, X. Cui, S. Venkataramani, K. Maghraoui, V. Srinivasan, and K. Gopalakrishnan. NeurIPS, (2020)FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training.Y. Fu, H. You, Y. Zhao, Y. Wang, C. Li, K. Gopalakrishnan, Z. Wang, and Y. Lin. NeurIPS, (2020)A Scalable Multi- TeraOPS Deep Learning Processor Core for AI Trainina and Inference.B. Fleischer, S. Shukla, M. Ziegler, J. Silberman, J. Oh, V. Srinivasan, J. Choi, S. Mueller, A. Agrawal, T. Babinsky and 21 other author(s). VLSI Circuits, page 35-36. IEEE, (2018)A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel.E. Ogawa, K. Ishizaki, H. Inoue, S. Venkataramani, J. Choi, W. Wang, V. Srinivasan, M. Ohara, and K. Gopalakrishnan. COOL CHIPS, page 1-3. IEEE, (2019)A 3.0 TFLOPS 0.62V Scalable Processor Core for High Compute Utilization AI Training and Inference.J. Oh, S. Lee, M. Kang, M. Ziegler, J. Silberman, A. Agrawal, S. Venkataramani, B. Fleischer, M. Guillorn, J. Choi and 33 other author(s). VLSI Circuits, page 1-2. IEEE, (2020)ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training.C. Chen, J. Ni, S. Lu, X. Cui, P. Chen, X. Sun, N. Wang, S. Venkataramani, V. Srinivasan, W. Zhang and 1 other author(s). NeurIPS, (2020)RaPiD: AI Accelerator for Ultra-low Precision Training and Inference.S. Venkataramani, V. Srinivasan, W. Wang, S. Sen, J. Zhang, A. Agrawal, M. Kar, S. Jain, A. Mannari, H. Tran and 44 other author(s). ISCA, page 153-166. IEEE, (2021)Efficient Management of Scratch-Pad Memories in Deep Learning Accelerators.S. Pal, S. Venkataramani, V. Srinivasan, and K. Gopalakrishnan. ISPASS, page 240-242. IEEE, (2021)

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Disambiguation of "Gopalakrishnan, Kailash"

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

Please choose a person to relate this publication to

Saiprasath Gopalakrishnan

Selvi Gopalakrishnan

Gopalakrishnan Venkiteswaran

Vijay Gopalakrishnan

Kailash Mogare

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Gopalakrishnan, Kailash"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Accumulation Bit-Width Scaling For Ultra-Low Precision Training Of Deep Networks.

Please choose a person to relate this publication to

Saiprasath Gopalakrishnan

Selvi Gopalakrishnan

Gopalakrishnan Venkiteswaran

Vijay Gopalakrishnan

Kailash Mogare

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
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MSOffice XML

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