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VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference.

S. Dai, R. Venkatesan, M. Ren, B. Zimmer, W. Dally, and B. Khailany. MLSys, mlsys.org, (2021)

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AutoCRAFT: Layout Automation for Custom Circuits in Advanced FinFET Technologies.H. Chen, W. Turner, S. Song, K. Zhu, G. Kokai, B. Zimmer, C. Gray, B. Khailany, D. Pan, and H. Ren. ISPD, page 175-183. ACM, (2022)Accelerating Chip Design with Machine Learning.B. Khailany. MLCAD, page 33. ACM, (2020)From RTL to CUDA: A GPU Acceleration Flow for RTL Simulation with Batch Stimulus.D. Lin, H. Ren, Y. Zhang, B. Khailany, and T. Huang. ICPP, page 88:1-88:12. ACM, (2022)Generative self-supervised learning for gate sizing: invited.S. Nath, G. Pradipta, C. Hu, T. Yang, B. Khailany, and H. Ren. DAC, page 1331-1334. ACM, (2022)FIST: A Feature-Importance Sampling and Tree-Based Method for Automatic Design Flow Parameter Tuning.Z. Xie, G. Fang, Y. Huang, H. Ren, Y. Zhang, B. Khailany, S. Fang, J. Hu, Y. Chen, and E. Barboza. ASP-DAC, page 19-25. IEEE, (2020)GPU design in a power-limited era.B. Khailany. MSE, page 68. IEEE Computer Society, (2013)Timeloop: A Systematic Approach to DNN Accelerator Evaluation.A. Parashar, P. Raina, Y. Shao, Y. Chen, V. Ying, A. Mukkara, R. Venkatesan, B. Khailany, S. Keckler, and J. Emer. ISPASS, page 304-315. IEEE, (2019)MAVIREC: ML-Aided Vectored IR-DropEstimation and Classification.V. Chhabria, Y. Zhang, H. Ren, B. Keller, B. Khailany, and S. Sapatnekar. CoRR, (2020)A 17-95.6 TOPS/W Deep Learning Inference Accelerator with Per-Vector Scaled 4-bit Quantization for Transformers in 5nm.B. Keller, R. Venkatesan, S. Dai, S. Tell, B. Zimmer, W. Dally, C. Gray, and B. Khailany. VLSI Technology and Circuits, page 16-17. IEEE, (2022)Optimizing VLSI Implementation with Reinforcement Learning - ICCAD Special Session Paper.H. Ren, S. Godil, B. Khailany, R. Kirby, H. Liao, S. Nath, J. Raiman, and R. Roy. ICCAD, page 1-6. IEEE, (2021)

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Disambiguation of "Khailany, Brucek"

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VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference.

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VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference.