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HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology.

, , , , , , , , , и . ACM Trans. Embed. Comput. Syst., 20 (5s): 66:1-66:25 (2021)

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Rohit Sinha

Slavica Manojlovic

Rohit Gupta

Rohit Ghai

Manoj Kakkassery

 

Другие публикации лиц с тем же именем

Hardware Aware Robust Compression of Neural Networks.. Technical University of Munich, Germany, (2023)Mind the Scaling Factors: Resilience Analysis of Quantized Adversarially Robust CNNs., , , , , , , и . DATE, стр. 706-711. IEEE, (2022)Adversarial Robust Model Compression Using In-Train Pruning., , , , , , , , , и 2 other автор(ы). CVPR Workshops, стр. 66-75. Computer Vision Foundation / IEEE, (2021)ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks., , , , , , и . DAC, стр. 1-6. IEEE, (2020)AnaCoNGA: Analytical HW-CNN Co-Design Using Nested Genetic Algorithms., , , , , , , , , и 3 other автор(ы). DATE, стр. 238-243. IEEE, (2022)Hardware-Aware Mixed-Precision Neural Networks using In-Train Quantization., , , , , , , , и . BMVC, стр. 60. BMVA Press, (2021)BreakingBED: Breaking Binary and Efficient Deep Neural Networks by Adversarial Attacks., , , , , , , , , и 1 other автор(ы). IntelliSys (1), том 294 из Lecture Notes in Networks and Systems, стр. 148-167. Springer, (2021)Investigating Binary Neural Networks for Traffic Sign Detection and Recognition., , , , , , , и . IV, стр. 1400-1405. IEEE, (2021)An Efficient FPGA Accelerator Design for Optimized CNNs Using OpenCL., , и . ARCS, том 11479 из Lecture Notes in Computer Science, стр. 236-249. Springer, (2019)HW-FlowQ: A Multi-Abstraction Level HW-CNN Co-design Quantization Methodology., , , , , , , , , и . ACM Trans. Embed. Comput. Syst., 20 (5s): 66:1-66:25 (2021)