Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks

3 May 2017

Papers citing "Compressing DMA Engine: Leveraging Activation Sparsity for Training Deep Neural Networks"

21 / 21 papers shown

Title
Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization Minsu Kim Seongmin Hong RyeoWook Ko S. Choi Hunjong Lee Junsoo Kim J. Kim Jongse Park 57 0 0 24 Mar 2025
Vision Transformers for Mobile Applications: A Short Survey Nahid Alam Steven Kolawole S. Sethi Nishant Bansali Karina Nguyen ViT 16 3 0 30 May 2023
SPADE: Sparse Pillar-based 3D Object Detection Accelerator for Autonomous Driving Minjae Lee Seongmin Park Hyung-Se Kim Minyong Yoon Jangwhan Lee Junwon Choi Nam Sung Kim Mingu Kang Jungwook Choi 3DPC 26 4 0 12 May 2023
Improved Projection Learning for Lower Dimensional Feature Maps Ilan Price Jared Tanner 8 2 0 27 Oct 2022
SmartSAGE: Training Large-scale Graph Neural Networks using In-Storage Processing Architectures Yunjae Lee Jin-Won Chung Minsoo Rhu GNN 27 48 0 10 May 2022
Training Personalized Recommendation Systems from (GPU) Scratch: Look Forward not Backwards Youngeun Kwon Minsoo Rhu 16 27 0 10 May 2022
GROW: A Row-Stationary Sparse-Dense GEMM Accelerator for Memory-Efficient Graph Convolutional Neural Networks Ranggi Hwang M. Kang Jiwon Lee D. Kam Youngjoo Lee Minsoo Rhu GNN 11 20 0 01 Mar 2022
COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression Sian Jin Chengming Zhang Xintong Jiang Yunhe Feng Hui Guan Guanpeng Li S. Song Dingwen Tao 20 23 0 18 Nov 2021
DynO: Dynamic Onloading of Deep Neural Networks from Cloud to Device Mario Almeida Stefanos Laskaridis Stylianos I. Venieris Ilias Leontiadis Nicholas D. Lane 13 36 0 20 Apr 2021
Extending Sparse Tensor Accelerators to Support Multiple Compression Formats Eric Qin Geonhwa Jeong William Won Sheng-Chun Kao Hyoukjun Kwon S. Srinivasan Dipankar Das G. Moon S. Rajamanickam T. Krishna 15 18 0 18 Mar 2021
A Novel Memory-Efficient Deep Learning Training Framework via Error-Bounded Lossy Compression Sian Jin Guanpeng Li S. Song Dingwen Tao AI4CE 19 12 0 18 Nov 2020
LazyBatching: An SLA-aware Batching System for Cloud Machine Learning Inference Yujeong Choi Yunseong Kim Minsoo Rhu 11 66 0 25 Oct 2020
Tensor Casting: Co-Designing Algorithm-Architecture for Personalized Recommendation Training Youngeun Kwon Yunjae Lee Minsoo Rhu 13 39 0 25 Oct 2020
FPRaker: A Processing Element For Accelerating Neural Network Training Omar Mohamed Awad Mostafa Mahmoud Isak Edo Vivancos Ali Hadi Zadeh Ciaran Bannon Anand Jayarajan Gennady Pekhimenko Andreas Moshovos 15 15 0 15 Oct 2020
SPINN: Synergistic Progressive Inference of Neural Networks over Device and Cloud Stefanos Laskaridis Stylianos I. Venieris Mario Almeida Ilias Leontiadis Nicholas D. Lane 22 265 0 14 Aug 2020
Sparse Weight Activation Training Md Aamir Raihan Tor M. Aamodt 32 72 0 07 Jan 2020
DASNet: Dynamic Activation Sparsity for Neural Network Efficiency Improvement Qing Yang Jiachen Mao Zuoguan Wang H. Li 13 15 0 13 Sep 2019
Neural Network Model Extraction Attacks in Edge Devices by Hearing Architectural Hints Xing Hu Ling Liang Lei Deng Shuangchen Li Xinfeng Xie Yu Ji Yufei Ding Chang Liu T. Sherwood Yuan Xie AAML MLAU 13 36 0 10 Mar 2019
Analyzing Machine Learning Workloads Using a Detailed GPU Simulator Jonathan Lew Deval Shah Suchita Pati Shaylin Cattell Mengchi Zhang ... Christopher Ng Negar Goli Matthew D. Sinclair Timothy G. Rogers Tor M. Aamodt 13 65 0 18 Nov 2018
Echo: Compiler-based GPU Memory Footprint Reduction for LSTM RNN Training Bojian Zheng Abhishek Tiwari Nandita Vijaykumar Gennady Pekhimenko 13 44 0 22 May 2018
Deeply learned face representations are sparse, selective, and robust Yi Sun Xiaogang Wang Xiaoou Tang CVBM 248 921 0 03 Dec 2014