SLID: Exploiting Spatial Locality in Input Data as a Computational Reuse Method for Efficient CNN
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[1] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Andrew Lavin,et al. Fast Algorithms for Convolutional Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[3] A.I. Sosin,et al. Optimization of Neural Network Computation with use of Residual Number System for Tasks of Design of Neural Network Systems of Automatic Control , 2018, 2018 International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon).
[4] David A. Patterson,et al. In-datacenter performance analysis of a tensor processing unit , 2017, 2017 ACM/IEEE 44th Annual International Symposium on Computer Architecture (ISCA).
[5] Bo Chen,et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications , 2017, ArXiv.
[6] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, ArXiv.
[7] Rajesh K. Gupta,et al. Energy-efficient neural networks using approximate computation reuse , 2018, 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).
[8] Jose-Maria Arnau,et al. Computation Reuse in DNNs by Exploiting Input Similarity , 2018, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).
[9] Said F. Al-Sarawi,et al. Memristor-Based Hardware Accelerator for Image Compression , 2018, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.
[10] Xipeng Shen,et al. Adaptive Deep Reuse: Accelerating CNN Training on the Fly , 2019, 2019 IEEE 35th International Conference on Data Engineering (ICDE).
[11] Xin He,et al. Exploiting the Potential of Computation Reuse Through Approximate Computing , 2017, IEEE Transactions on Multi-Scale Computing Systems.
[12] Meng-Fan Chang,et al. A 65nm 1Mb nonvolatile computing-in-memory ReRAM macro with sub-16ns multiply-and-accumulate for binary DNN AI edge processors , 2018, 2018 IEEE International Solid - State Circuits Conference - (ISSCC).
[13] Xipeng Shen,et al. Deep reuse: streamline CNN inference on the fly via coarse-grained computation reuse , 2019, ICS.
[14] Yann LeCun,et al. The mnist database of handwritten digits , 2005 .
[15] Luigi Carro,et al. Skipping CNN Convolutions Through Efficient Memoization , 2019, SAMOS.
[16] Baker Mohammad,et al. NeuroMem: Analog Graphene-Based Resistive Memory for Artificial Neural Networks , 2020, Scientific Reports.
[17] Catherine Graves,et al. Dot-product engine for neuromorphic computing: Programming 1T1M crossbar to accelerate matrix-vector multiplication , 2016, 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC).
[18] Youfeng Wu,et al. Better exploration of region-level value locality with integrated computation reuse and value prediction , 2001, ISCA 2001.
[19] Said F. Al-Sarawi,et al. ReRAM-Based In-Memory Computing for Search Engine and Neural Network Applications , 2019, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[20] Xiangyu Zhang,et al. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[21] Trevor Darrell,et al. Caffe: Convolutional Architecture for Fast Feature Embedding , 2014, ACM Multimedia.
[22] Mingyu Wang,et al. ERA-LSTM: An Efficient ReRAM-Based Architecture for Long Short-Term Memory , 2020, IEEE Transactions on Parallel and Distributed Systems.
[23] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[24] Tinoosh Mohsenin,et al. Accelerating Convolutional Neural Network With FFT on Embedded Hardware , 2018, IEEE Transactions on Very Large Scale Integration (VLSI) Systems.
[25] Song Guo,et al. Exploiting Computation Reuse in Cloud-Based Deep Learning via Input Reordering , 2020, ICC 2020 - 2020 IEEE International Conference on Communications (ICC).
[26] Mengjia Yan,et al. UCNN: Exploiting Computational Reuse in Deep Neural Networks via Weight Repetition , 2018, 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA).
[27] François Chollet,et al. Xception: Deep Learning with Depthwise Separable Convolutions , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[28] Baker Mohammad,et al. Modeling Valance Change Memristor Device: Oxide Thickness, Material Type, and Temperature Effects , 2016, IEEE Transactions on Circuits and Systems I: Regular Papers.
[29] Huanrui Yang,et al. AtomLayer: A Universal ReRAM-Based CNN Accelerator with Atomic Layer Computation , 2018, 2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC).
[30] Azadeh Davoodi,et al. Efficient Inference of CNNs via Channel Pruning , 2019, ArXiv.