Quantizing deep convolutional networks for efficient inference: A whitepaper

21 June 2018

Papers citing "Quantizing deep convolutional networks for efficient inference: A whitepaper"

50 / 465 papers shown

Title
Bias Loss for Mobile Neural Networks L. Abrahamyan Valentin Ziatchin Yiming Chen Nikos Deligiannis 15 14 0 23 Jul 2021
A High-Performance Adaptive Quantization Approach for Edge CNN Applications Hsu-Hsun Chin R. Tsay Hsin-I Wu MQ 8 5 0 18 Jul 2021
LANA: Latency Aware Network Acceleration Pavlo Molchanov Jimmy Hall Hongxu Yin Jan Kautz Nicolò Fusi Arash Vahdat 25 11 0 12 Jul 2021
Q-SpiNN: A Framework for Quantizing Spiking Neural Networks Rachmad Vidya Wicaksana Putra Muhammad Shafique MQ 11 44 0 05 Jul 2021
Popcorn: Paillier Meets Compression For Efficient Oblivious Neural Network Inference Jun Wang Chao Jin S. Meftah Khin Mi Mi Aung UQCV 16 3 0 05 Jul 2021
PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation Jang-Hyun Kim Simyung Chang Nojun Kwak 22 44 0 25 Jun 2021
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better Gaurav Menghani VLM MedIm 23 365 0 16 Jun 2021
A White Paper on Neural Network Quantization Markus Nagel Marios Fournarakis Rana Ali Amjad Yelysei Bondarenko M. V. Baalen Tijmen Blankevoort MQ 19 503 0 15 Jun 2021
Neuroevolution-Enhanced Multi-Objective Optimization for Mixed-Precision Quantization Santiago Miret Vui Seng Chua Mattias Marder Mariano Phielipp Nilesh Jain Somdeb Majumdar 13 8 0 14 Jun 2021
Rethinking Transfer Learning for Medical Image Classification Le Peng Hengyue Liang Gaoxiang Luo Taihui Li Ju Sun VLM LM&MA 14 5 0 09 Jun 2021
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization Yukuan Yang Xiaowei Chi Lei Deng Tianyi Yan Feng Gao Guoqi Li MQ 23 6 0 27 May 2021
Low-Precision Hardware Architectures Meet Recommendation Model Inference at Scale Zhaoxia Deng Deng Jongsoo Park P. T. P. Tang Haixin Liu ... S. Nadathur Changkyu Kim Maxim Naumov S. Naghshineh M. Smelyanskiy 15 11 0 26 May 2021
Post-Training Sparsity-Aware Quantization Gil Shomron F. Gabbay Samer Kurzum U. Weiser MQ 33 33 0 23 May 2021
BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer Haoping Bai Mengsi Cao Ping-Chia Huang Jiulong Shan MQ 17 34 0 19 May 2021
Rethinking "Batch" in BatchNorm Yuxin Wu Justin Johnson BDL 37 66 0 17 May 2021
Is In-Domain Data Really Needed? A Pilot Study on Cross-Domain Calibration for Network Quantization Haichao Yu Linjie Yang Humphrey Shi OOD MQ 24 5 0 16 May 2021
Lightweight Compression of Intermediate Neural Network Features for Collaborative Intelligence R. Cohen Hyomin Choi Ivan V. Bajić 19 23 0 15 May 2021
Lightweight compression of neural network feature tensors for collaborative intelligence R. Cohen Hyomin Choi Ivan V. Bajić 22 42 0 12 May 2021
In-Hindsight Quantization Range Estimation for Quantized Training Marios Fournarakis Markus Nagel MQ 14 10 0 10 May 2021
Stealthy Backdoors as Compression Artifacts Yulong Tian Fnu Suya Fengyuan Xu David E. Evans 27 22 0 30 Apr 2021
Memory-Efficient Deep Learning Inference in Trusted Execution Environments Jean-Baptiste Truong W. Gallagher Tian Guo R. Walls 17 8 0 30 Apr 2021
Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamics S. Yun Alexander Wong MQ 19 25 0 24 Apr 2021
Differentiable Model Compression via Pseudo Quantization Noise Alexandre Défossez Yossi Adi Gabriel Synnaeve DiffM MQ 15 47 0 20 Apr 2021
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators David Stutz Nandhini Chandramoorthy Matthias Hein Bernt Schiele AAML MQ 22 18 0 16 Apr 2021
TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT H. F. Langroudi Vedant Karia Tej Pandit Dhireesha Kudithipudi MQ 19 10 0 06 Apr 2021
RCT: Resource Constrained Training for Edge AI Tian Huang Tao Luo Ming Yan Joey Tianyi Zhou Rick Siow Mong Goh 25 8 0 26 Mar 2021
Learned Gradient Compression for Distributed Deep Learning L. Abrahamyan Yiming Chen Giannis Bekoulis Nikos Deligiannis 32 45 0 16 Mar 2021
Quantization-Guided Training for Compact TinyML Models Sedigh Ghamari Koray Ozcan Thu Dinh A. Melnikov Juan Carvajal Jan Ernst S. Chai MQ 16 16 0 10 Mar 2021
unzipFPGA: Enhancing FPGA-based CNN Engines with On-the-Fly Weights Generation Stylianos I. Venieris Javier Fernandez-Marques Nicholas D. Lane 24 11 0 09 Mar 2021
Reliability-Aware Quantization for Anti-Aging NPUs Sami Salamin Georgios Zervakis Ourania Spantidi Iraklis Anagnostopoulos J. Henkel H. Amrouch 8 13 0 08 Mar 2021
Compiler Toolchains for Deep Learning Workloads on Embedded Platforms Max Sponner Bernd Waschneck Akash Kumar MQ 12 5 0 08 Mar 2021
COIN: COmpression with Implicit Neural representations Emilien Dupont Adam Goliñski Milad Alizadeh Yee Whye Teh Arnaud Doucet 20 223 0 03 Mar 2021
On the Effects of Quantisation on Model Uncertainty in Bayesian Neural Networks Martin Ferianc Partha P. Maji Matthew Mattina Miguel R. D. Rodrigues UQCV BDL 14 9 0 22 Feb 2021
PLAM: a Posit Logarithm-Approximate Multiplier Raul Murillo Alberto A. Del Barrio Guillermo Botella Min Soo Kim Hyunjin Kim N. Bagherzadeh TPM 24 25 0 18 Feb 2021
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware Transformation Chaofan Tao Rui Lin Quan Chen Zhaoyang Zhang Ping Luo Ngai Wong MQ 26 7 0 15 Feb 2021
Confounding Tradeoffs for Neural Network Quantization Sahaj Garg Anirudh Jain Joe Lou Mitchell Nahmias MQ 21 17 0 12 Feb 2021
Dynamic Precision Analog Computing for Neural Networks Sahaj Garg Joe Lou Anirudh Jain Mitchell Nahmias 37 33 0 12 Feb 2021
VS-Quant: Per-vector Scaled Quantization for Accurate Low-Precision Neural Network Inference Steve Dai Rangharajan Venkatesan Haoxing Ren B. Zimmer W. Dally Brucek Khailany MQ 25 67 0 08 Feb 2021
Real-time Denoising and Dereverberation with Tiny Recurrent U-Net Hyeong-Seok Choi Sungjin Park Jie Hwan Lee Hoon Heo Dongsuk Jeon Kyogu Lee 34 57 0 05 Feb 2021
Fixed-point Quantization of Convolutional Neural Networks for Quantized Inference on Embedded Platforms Rishabh Goyal Joaquin Vanschoren V. V. Acht S. Nijssen MQ 22 23 0 03 Feb 2021
Rethinking Floating Point Overheads for Mixed Precision DNN Accelerators Hamzah Abdel-Aziz Ali Shafiee J. Shin A. Pedram Joseph Hassoun MQ 34 10 0 27 Jan 2021
Pruning and Quantization for Deep Neural Network Acceleration: A Survey Tailin Liang C. Glossner Lei Wang Shaobo Shi Xiaotong Zhang MQ 130 673 0 24 Jan 2021
MinConvNets: A new class of multiplication-less Neural Networks Xuecan Yang S. Chaudhuri Laurence Likforman L. Naviner 14 0 0 23 Jan 2021
Generative Zero-shot Network Quantization Xiangyu He Qinghao Hu Peisong Wang Jian Cheng GAN MQ 26 23 0 21 Jan 2021
Network Pruning using Adaptive Exemplar Filters Mingbao Lin Rongrong Ji Shaojie Li Yan Wang Yongjian Wu Feiyue Huang QiXiang Ye VLM 11 53 0 20 Jan 2021
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS F. Dellinger T. Boulay Diego Mendoza Barrenechea Said El-Hachimi Isabelle Leang Fabian Burger 14 2 0 19 Jan 2021
KDLSQ-BERT: A Quantized Bert Combining Knowledge Distillation with Learned Step Size Quantization Jing Jin Cai Liang Tiancheng Wu Li Zou Zhiliang Gan MQ 17 26 0 15 Jan 2021
NetCut: Real-Time DNN Inference Using Layer Removal Mehrshad Zandigohar Deniz Erdogmus G. Schirner 15 5 0 13 Jan 2021
A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks Asaf Noy Yi Tian Xu Y. Aflalo Lihi Zelnik-Manor R. L. Jin 31 3 0 12 Jan 2021
I-BERT: Integer-only BERT Quantization Sehoon Kim A. Gholami Z. Yao Michael W. Mahoney Kurt Keutzer MQ 96 341 0 05 Jan 2021