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The large computing and memory cost of deep neural networks (DNNs) often precludes their use in resource-constrained devices.
Imagenet: A large-scale hierarchical image database
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Google vizier: A service for black-box optimization
Daniel Golovin, Benjamin Solnik, Subhodeep Moitra, Greg Kochanski, John Karro, and D. Sculley · 2017
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In-datacenter performance analysis of a tensor processing unit
Norman P Jouppi, Cliff Young, Nishant Patil, David Patterson, Gaurav Agrawal, Raminder Bajwa, Sarah Bates, Suresh Bhatia, Nan Boden, Al Borchers, et al · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Pact: Parameterized clipping activation for quantized neural networks
Jungwook Choi, Zhuo Wang, Swagath Venkataramani, Pierce I-Jen Chuang, Vijayalakshmi Srinivasan, and Kailash Gopalakrishnan · 2018
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Releq: A reinforcement learning approach for deep quantization of neural networks
Ahmed T Elthakeb, Prannoy Pilligundla, FatemehSadat Mireshghallah, Amir Yazdanbakhsh, and Hadi Esmaeilzadeh · 2018
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Squeezenext: Hardware-aware neural network design
Amir Gholami, Kiseok Kwon, Bichen Wu, Zizheng Tai, Xiangyu Yue, Peter Jin, Sicheng Zhao, and Kurt Keutzer · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, and Liang-Chieh Chen · 2018
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Post training 4-bit quantization of convolutional networks for rapid-deployment
Ron Banner, Yury Nahshan, and Daniel Soudry · 2019
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Eyeriss v2: A flexible accelerator for emerging deep neural networks on mobile devices
Yu-Hsin Chen, Tien-Ju Yang, Joel Emer, and Vivienne Sze · 2019
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Learned step size quantization
Steven K Esser, Jeffrey L McKinstry, Deepika Bablani, Rathinakumar Appuswamy, and Dharmendra S Modha · 2019
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Differentiable soft quantization: Bridging full-precision and low-bit neural networks
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Sara Hooker, Aaron Courville, Gregory Clark, Yann Dauphin, and Andrea Frome · 2019
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Sangil Jung, Changyong Son, Seohyung Lee, Jinwoo Son, Jae-Joon Han, Youngjun Kwak, Sung Ju Hwang, and Changkyu Choi · 2019
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Megatron-LM: Training multi-billion parameter language models using model parallelism
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
When vision transformers outperform resnets without pre-training or strong data augmentations
Xiangning Chen, Cho-Jui Hsieh, and Boqing Gong · 2021
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Steve Dai, Rangha Venkatesan, Mark Ren, Brian Zimmer, William Dally, and Brucek Khailany · 2021
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Survey and benchmarking of precision-scalable mac arrays for embedded dnn processing
Ehab M Ibrahim, Linyan Mei, and Marian Verhelst · 2021
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Network quantization with element-wise gradient scaling
Junghyup Lee, Dohyung Kim, and Bumsub Ham · 2021
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Mixed precision dnns: All you need is a good parametrization
Stefan Uhlich, Lukas Mauch, Fabien Cardinaux, Kazuki Yoshiyama, Javier Alonso Garcia, Stephen Tiedemann, Thomas Kemp, and Akira Nakamura · 2019
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Haq: Hardware-aware automated quantization with mixed precision
Kuan Wang, Zhijian Liu, Yujun Lin, Ji Lin, and Song Han · 2019
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Accelergy: An architecture-level energy estimation methodology for accelerator designs
Yannan Nellie Wu, Joel S Emer, and Vivienne Sze · 2019
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Lsq+: Improving low-bit quantization through learnable offsets and better initialization
Yash Bhalgat, Jinwon Lee, Markus Nagel, Tijmen Blankevoort, and Nojun Kwak · 2020
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Hawq-v2: Hessian aware trace-weighted quantization of neural networks
Zhen Dong, Zhewei Yao, Daiyaan Arfeen, Amir Gholami, Michael W Mahoney, and Kurt Keutzer · 2020
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Flax: A neural network library and ecosystem for JAX, 2020
Jonathan Heek, Anselm Levskaya, Avital Oliver, Marvin Ritter, Bertrand Rondepierre, Andreas Steiner, and Marc van Zee · 2020
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Optax: composable gradient transformation and optimisation, in jax!, 2020
Matteo Hessel, David Budden, Fabio Viola, Mihaela Rosca, Eren Sezener, and Tom Hennigan · 2020
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Weier Wan, Rajkumar Kubendran, Clemens Schaefer, S Burc Eryilmaz, Wenqiang Zhang, Dabin Wu, Stephen Deiss, Priyanka Raina, He Qian, Bin Gao, et al · 2021
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Hawq-v3: Dyadic neural network quantization
Zhewei Yao, Zhen Dong, Zhangcheng Zheng, Amir Gholami, Jiali Yu, Eric Tan, Leyuan Wang, Qijing Huang, Yida Wang, Michael Mahoney, et al · 2021
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An evaluation of edge tpu accelerators for convolutional neural networks
Amir Yazdanbakhsh, Kiran Seshadri, Berkin Akin, James Laudon, and Ravi Narayanaswami · 2021
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Xiaohua Zhai, Alexander Kolesnikov, Neil Houlsby, and Lucas Beyer · 2021
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Darkside: A heterogeneous risc-v compute cluster for extreme-edge on-chip dnn inference and training
Angelo Garofalo, Yvan Tortorella, Matteo Perotti, Luca Valente, Alessandro Nadalini, Luca Benini, Davide Rossi, and Francesco Conti · 2022
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Precision-aware latency and energy balancing on multi-accelerator platforms for dnn inference
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Free bits: Latency optimization of mixed-precision quantized neural networks on the edge
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