暂无分享,去创建一个
Matthew Mattina | Paul N. Whatmough | Vijay Janapa Reddi | Igor Fedorov | Dibakar Gope | Colby Banbury | Chuteng Zhou | Ramon Matas Navarro | Urmish Thakkar | Colby R. Banbury | Ramon Matas Navarro | P. Whatmough | V. Reddi | C. Banbury | Urmish Thakker | Matthew Mattina | Dibakar Gope | Igor Fedorov | Chuteng Zhou
[1] V. Reddi,et al. TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systems , 2020, MLSys.
[2] Di Niu,et al. Neural Architecture Search For Keyword Spotting , 2020, INTERSPEECH.
[3] Luca Benini,et al. Leveraging Automated Mixed-Low-Precision Quantization for Tiny Edge Microcontrollers , 2020, IoT Streams/ITEM@PKDD/ECML.
[4] Eunhyeok Park,et al. PROFIT: A Novel Training Method for sub-4-bit MobileNet Models , 2020, ECCV.
[5] A. Wong,et al. TinySpeech: Attention Condensers for Deep Speech Recognition Neural Networks on Edge Devices , 2020, ArXiv.
[6] Song Han,et al. MCUNet: Tiny Deep Learning on IoT Devices , 2020, NeurIPS.
[7] Paulo Cortez,et al. Deep Dense and Convolutional Autoencoders for Unsupervised Anomaly Detection in Machine Condition Sounds , 2020, ArXiv.
[8] Matthew Mattina,et al. TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids , 2020, INTERSPEECH.
[9] Yuandong Tian,et al. FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[10] Luca Benini,et al. CMix-NN: Mixed Low-Precision CNN Library for Memory-Constrained Edge Devices , 2020, IEEE Transactions on Circuits and Systems II: Express Briefs.
[11] David Patterson,et al. Benchmarking TinyML Systems: Challenges and Direction , 2020, ArXiv.
[12] Matthew Mattina,et al. Ternary MobileNets via Per-Layer Hybrid Filter Banks , 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[13] Steven K. Esser,et al. Learned Step Size Quantization , 2019, ICLR.
[14] A. Krishnaswamy,et al. UNSUPERVISED ANOMALOUS SOUND DETECTION USING SELF-SUPERVISED CLASSIFICATION AND GROUP MASKED AUTOENCODER FOR DENSITY ESTIMATION Technical , 2020 .
[15] Shouyi Yin,et al. Small-Footprint Keyword Spotting with Graph Convolutional Network , 2019, 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU).
[16] Yukun Yang,et al. MSNet: Structural Wired Neural Architecture Search for Internet of Things , 2019, 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW).
[17] Yohei Kawaguchi,et al. MIMII Dataset: Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection , 2019, DCASE.
[18] David Gregg,et al. Performance-Oriented Neural Architecture Search , 2019, 2019 International Conference on High Performance Computing & Simulation (HPCS).
[19] Dawn Song,et al. Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty , 2019, NeurIPS.
[20] Aakanksha Chowdhery,et al. Visual Wake Words Dataset , 2019, ArXiv.
[21] Matthew Mattina,et al. Compressing RNNs for IoT devices by 15-38x using Kronecker Products , 2019, ArXiv.
[22] Ryan P. Adams,et al. SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers , 2019, NeurIPS.
[23] Boris Murmann,et al. Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications , 2019, ICML.
[24] Yi Yang,et al. Network Pruning via Transformable Architecture Search , 2019, NeurIPS.
[25] Dongyoung Kim,et al. Temporal Convolution for Real-time Keyword Spotting on Mobile Devices , 2019, INTERSPEECH.
[26] Matthew Mattina,et al. Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications , 2019, MLSys.
[27] Patrick Hansen,et al. FixyNN: Efficient Hardware for Mobile Computer Vision via Transfer Learning , 2019, ArXiv.
[28] Daniel Soudry,et al. Post training 4-bit quantization of convolutional networks for rapid-deployment , 2018, NeurIPS.
[29] Song Han,et al. ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware , 2018, ICLR.
[30] Frank Hutter,et al. Neural Architecture Search: A Survey , 2018, J. Mach. Learn. Res..
[31] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[32] Prateek Jain,et al. FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network , 2018, NeurIPS.
[33] Raghuraman Krishnamoorthi,et al. Quantizing deep convolutional networks for efficient inference: A whitepaper , 2018, ArXiv.
[34] Pete Warden,et al. Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition , 2018, ArXiv.
[35] Vikas Chandra,et al. CMSIS-NN: Efficient Neural Network Kernels for Arm Cortex-M CPUs , 2018, ArXiv.
[36] Mark Sandler,et al. MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[37] Vikas Chandra,et al. Not All Ops Are Created Equal! , 2018, ArXiv.
[38] Hongyi Zhang,et al. mixup: Beyond Empirical Risk Minimization , 2017, ICLR.
[39] Yundong Zhang,et al. Hello Edge: Keyword Spotting on Microcontrollers , 2017, ArXiv.
[40] Saurabh Goyal,et al. Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things , 2017, ICML.
[41] Prateek Jain,et al. ProtoNN: Compressed and Accurate kNN for Resource-scarce Devices , 2017, ICML.
[42] Sandip Parikh,et al. High performance DSP for vision, imaging and neural networks , 2016, 2016 IEEE Hot Chips 28 Symposium (HCS).
[43] И. И. Балаш. Противоугонное устройство с радиочастотной идентификацией на базе микроконтроллера семейства ARM CORTEX-M4 , 2016 .
[44] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[45] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[46] David A. Patterson,et al. Computer Architecture, Fifth Edition: A Quantitative Approach , 2011 .