SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
暂无分享,去创建一个
Yue Wang | Yang Zhao | Chaojian Li | Yingyan Lin | Zhangyang Wang | Yuan Xie | Haoran You | Yonggan Fu | Xiaohan Chen | Zhangyang Wang | Yuan Xie | Haoran You | Chaojian Li | Y. Fu | Yue Wang | Xiaohan Chen | Yingyan Lin | Yang Zhao
[1] Dacheng Tao,et al. On Compressing Deep Models by Low Rank and Sparse Decomposition , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[3] Marian Verhelst,et al. An always-on 3.8μJ/86% CIFAR-10 mixed-signal binary CNN processor with all memory on chip in 28nm CMOS , 2018, 2018 IEEE International Solid - State Circuits Conference - (ISSCC).
[4] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[5] Eugenio Culurciello,et al. Flattened Convolutional Neural Networks for Feedforward Acceleration , 2014, ICLR.
[6] Tianshi Chen,et al. ShiDianNao: Shifting vision processing closer to the sensor , 2015, 2015 ACM/IEEE 42nd Annual International Symposium on Computer Architecture (ISCA).
[7] Yinghuan Shi,et al. Accelerating Deep Neural Networks by Combining Block-Circulant Matrices and Low-Precision Weights , 2019 .
[8] Joel Emer,et al. Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks , 2016, CARN.
[9] Rohit Prabhavalkar,et al. Compressing deep neural networks using a rank-constrained topology , 2015, INTERSPEECH.
[10] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[11] Patrick Judd,et al. Stripes: Bit-serial deep neural network computing , 2016, 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[12] Roberto Cipolla,et al. Segmentation and Recognition Using Structure from Motion Point Clouds , 2008, ECCV.
[13] Iasonas Kokkinos,et al. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[14] Vivienne Sze,et al. Eyeriss v2: A Flexible Accelerator for Emerging Deep Neural Networks on Mobile Devices , 2018, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[15] Joan Bruna,et al. Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation , 2014, NIPS.
[16] Song Han,et al. Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding , 2015, ICLR.
[17] David Blaauw,et al. An 879GOPS 243mW 80fps VGA Fully Visual CNN-SLAM Processor for Wide-Range Autonomous Exploration , 2019, 2019 IEEE International Solid- State Circuits Conference - (ISSCC).
[18] Shuang Wu,et al. Training High-Performance and Large-Scale Deep Neural Networks with Full 8-bit Integers , 2020, Neural Networks.
[19] Zhiqiang Shen,et al. Learning Efficient Convolutional Networks through Network Slimming , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[20] Tianshi Chen,et al. Cambricon-S: Addressing Irregularity in Sparse Neural Networks through A Cooperative Software/Hardware Approach , 2018, 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[21] Ninghui Sun,et al. DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning , 2014, ASPLOS.
[22] Xiangyu Zhang,et al. Channel Pruning for Accelerating Very Deep Neural Networks , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[23] Shuchang Zhou,et al. DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients , 2016, ArXiv.
[24] Yue Wang,et al. Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions , 2018, ICML.
[25] Jianxin Wu,et al. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression , 2017, 2017 IEEE International Conference on Computer Vision (ICCV).
[26] Yue Wang,et al. Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference , 2019, IEEE Journal of Selected Topics in Signal Processing.
[27] Ming Yang,et al. Compressing Deep Convolutional Networks using Vector Quantization , 2014, ArXiv.
[28] William J. Dally,et al. SCNN: An accelerator for compressed-sparse convolutional neural networks , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).
[29] Song Han,et al. Learning both Weights and Connections for Efficient Neural Network , 2015, NIPS.
[30] Priyanka Raina,et al. DNN Dataflow Choice Is Overrated , 2018, ArXiv.
[31] Hanan Samet,et al. Pruning Filters for Efficient ConvNets , 2016, ICLR.
[32] Yuangang Wang,et al. A Highly Parallel and Energy Efficient Three-Dimensional Multilayer CMOS-RRAM Accelerator for Tensorized Neural Network , 2018, IEEE Transactions on Nanotechnology.
[33] Yu Wang,et al. Exploring the Granularity of Sparsity in Convolutional Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[34] Hui Liu,et al. On-Demand Deep Model Compression for Mobile Devices: A Usage-Driven Model Selection Framework , 2018, MobiSys.
[35] Charbel Sakr,et al. PredictiveNet: An energy-efficient convolutional neural network via zero prediction , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).
[36] Yue Wang,et al. E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings , 2019, NeurIPS.
[37] Yue Wang,et al. AutoDNNchip: An Automated DNN Chip Predictor and Builder for Both FPGAs and ASICs , 2020, FPGA.
[38] Song Han,et al. EIE: Efficient Inference Engine on Compressed Deep Neural Network , 2016, 2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA).
[39] Joel Emer,et al. Eyeriss: an Energy-efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks Accessed Terms of Use , 2022 .
[40] Naresh R. Shanbhag,et al. Variation-Tolerant Architectures for Convolutional Neural Networks in the Near Threshold Voltage Regime , 2016, 2016 IEEE International Workshop on Signal Processing Systems (SiPS).
[41] Patrick Judd,et al. Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks , 2019, ASPLOS.
[42] Elad Hoffer,et al. Scalable Methods for 8-bit Training of Neural Networks , 2018, NeurIPS.
[43] Daniel Brand,et al. Training Deep Neural Networks with 8-bit Floating Point Numbers , 2018, NeurIPS.
[44] David Blaauw,et al. A 1920 $\times$ 1080 25-Frames/s 2.4-TOPS/W Low-Power 6-D Vision Processor for Unified Optical Flow and Stereo Depth With Semi-Global Matching , 2019, IEEE Journal of Solid-State Circuits.
[45] Zhangyang Wang,et al. Adversarially Trained Model Compression: When Robustness Meets Efficiency , 2019, ArXiv.
[46] Hoi-Jun Yoo,et al. UNPU: A 50.6TOPS/W unified deep neural network accelerator with 1b-to-16b fully-variable weight bit-precision , 2018, 2018 IEEE International Solid - State Circuits Conference - (ISSCC).
[47] Shaoli Liu,et al. Cambricon-X: An accelerator for sparse neural networks , 2016, 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[48] Li Fei-Fei,et al. ImageNet: A large-scale hierarchical image database , 2009, CVPR.
[49] Jian Cheng,et al. Quantized Convolutional Neural Networks for Mobile Devices , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[50] Andreas Moshovos,et al. Bit-Pragmatic Deep Neural Network Computing , 2016, 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).
[51] Xianglong Liu,et al. Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).