Deep model quantization can be used for reducing the computation and memory costs of DNNs, and deploying complex DNNs on mobile equipment. In this work, we propose an optimization framework for deep model quant
ANT: Exploiting Adaptive Numerical Data Type for Low-Bit Deep Neural Network Quantization Cong Guo, Chen Zhang, Jingwen Leng, Zihan Liu, Fan Yang, Yunxin Liu, Minyi Guo, Yuhao Zhu MICRO|October 2022 IEEE Micro Top Picks 2023 Honorable Mention ...
Gholami, A., et al.: A survey of quantization methods for efficient neural network inference. Low-Power Computer Vision Book (2021) Google Scholar Guan, Y., et al.: FP-DNN: An automated framework for mapping deep neural networks onto FPGAs with RTL-HLS hybrid templates. In: FCCM Sympos...
Regardless of the deployment method, it is challenging for users to ensure that the model is fully deployed as intended by the owner. The model can be subjected to quantization or pruning to reduce server load, and it may also be vulnerable to attacks that modify the model parameters, such ...
(PIN) diodes26,27,28,29,30. It is a low-cost alternative to the expensive phase shifters. Although generally speaking, PMS performs a rough 1-bit or 2-bit quantization for the wavefront phase of electromagnetic (EM) wave31,32,33,34, and there is a gap in the efficiency of power ...
and 4-bit accuracy for both maps and weights, exploiting the resiliency of CNNs to quantization and approximation [21]. Even extreme approaches to quantization have been proposed, exploiting ternary [24] or binary [25] neural network accelerators for FPGA. This approach significantly improves the ...
Rawhash2: Mapping raw nanopore signals using hash-based seeding and adaptive quantization. Bioinformatics, btae 2024;478. Gamaarachchi H, Lam CW, Jayatilaka G, Samarakoon H, Simpson JT, Smith MA, Parameswaran S. Gpu accelerated adaptive banded event alignment for rapid comparative nanopore ...
For some applications, static precision scaling, like quantization, has already been applied in commercial hardware such as [113]. Though the benefits of this are clear, one must be conservative to guarantee output quality when applying static techniques. This has brought attention to adaptive dynami...
Advances in neural information processing systems 30 (2017) Ma, L., Luo, X., Hong, H., Meng, F., Wu, Q.: Logit variated product quantization based on parts interaction and metric learning with knowledge distillation for fine-grained image retrieval. IEEE Transactions on Multimedia 26, ...
(2017). Qsgd: Communication-efficient sgd via gradient quantization and encoding. In Advances in neural information processing systems (Vol. 30, pp. 1709–1720). Allen-Zhu, Z. (2018). How to make the gradients small stochastically: Even faster convex and nonconvex sgd. In Advances in ...