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Efficient AI System Design With Cross-Layer Approximate Computing.

S. Venkataramani, X. Sun, N. Wang, C. Chen, J. Choi, M. Kang, A. Agarwal, J. Oh, S. Jain, T. Babinsky, N. Cao, T. Fox, B. Fleischer, G. Gristede, M. Guillorn, H. Haynie, H. Inoue, K. Ishizaki, M. Klaiber, S. Lo, G. Maier, S. Mueller, M. Scheuermann, E. Ogawa, M. Schaal, M. Serrano, J. Silberman, C. Vezyrtzis, W. Wang, F. Yee, J. Zhang, M. Ziegler, C. Zhou, M. Ohara, P. Lu, B. Curran, S. Shukla, V. Srinivasan, L. Chang, and K. Gopalakrishnan. Proc. IEEE, 108 (12): 2232-2250 (2020)

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Taming the beast: Programming Peta-FLOP class Deep Learning Systems.S. Venkataramani, V. Srinivasan, J. Choi, K. Gopalakrishnan, and L. Chang. ISLPED, page 18:1. ACM, (2018)STOCK: Stochastic Checkers for Low-overhead Approximate Error Detection.N. Gala, S. Venkataramani, A. Raghunathan, and V. Kamakoti. ISLPED, page 266-271. ACM, (2016)BiScaled-DNN: Quantizing Long-tailed Datastructures with Two Scale Factors for Deep Neural Networks.S. Jain, S. Venkataramani, V. Srinivasan, J. Choi, K. Gopalakrishnan, and L. Chang. DAC, page 201. ACM, (2019)Compensated-DNN: energy efficient low-precision deep neural networks by compensating quantization errors.S. Jain, S. Venkataramani, V. Srinivasan, J. Choi, P. Chuang, and L. Chang. DAC, page 38:1-38:6. ACM, (2018)Approximate computing and the quest for computing efficiency.S. Venkataramani, S. Chakradhar, K. Roy, and A. Raghunathan. DAC, page 120:1-120:6. ACM, (2015)StoRM: a stochastic recognition and mining processor.V. Chippa, S. Venkataramani, K. Roy, and A. Raghunathan. ISLPED, page 39-44. ACM, (2014)Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks.X. Sun, J. Choi, C. Chen, N. Wang, S. Venkataramani, V. Srinivasan, X. Cui, W. Zhang, and K. Gopalakrishnan. NeurIPS, page 4901-4910. (2019)Exploring Spin-Transfer-Torque Devices for Logic Applications.Z. Pajouhi, S. Venkataramani, K. Yogendra, A. Raghunathan, and K. Roy. IEEE Trans. on CAD of Integrated Circuits and Systems, 34 (9): 1441-1454 (2015)Efficient embedded learning for IoT devices.S. Venkataramani, K. Roy, and A. Raghunathan. ASP-DAC, page 308-311. IEEE, (2016)Energy-Efficient Reduce-and-Rank Using Input-Adaptive Approximations.A. Raha, S. Venkataramani, V. Raghunathan, and A. Raghunathan. IEEE Trans. VLSI Syst., 25 (2): 462-475 (2017)

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Efficient AI System Design With Cross-Layer Approximate Computing.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Efficient AI System Design With Cross-Layer Approximate Computing.

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Efficient AI System Design With Cross-Layer Approximate Computing.