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RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
Neural Information Processing Systems (NeurIPS), 2020
27 February 2020
Oindrila Saha
Aditya Kusupati
H. Simhadri
Manik Varma
Prateek Jain
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Papers citing
"RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference"
23 / 23 papers shown
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MCUFormer: Deploying Vision Transformers on Microcontrollers with Limited Memory
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Enabling Resource-efficient AIoT System with Cross-level Optimization: A survey
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Multi-output Headed Ensembles for Product Item Classification
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A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy
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Pex: Memory-efficient Microcontroller Deep Learning through Partial Execution
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364
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Enabling ISP-less Low-Power Computer Vision
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Self-Attentive Pooling for Efficient Deep Learning
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16 Sep 2022
Machine Learning for Microcontroller-Class Hardware: A Review
IEEE Sensors Journal (IEEE Sens. J.), 2022
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Matryoshka Representation Learning
Neural Information Processing Systems (NeurIPS), 2022
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Ali Farhadi
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P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications
Scientific Reports (Sci Rep), 2022
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Souvik Kundu
Zihan Yin
R. T. Lakkireddy
Joe Mathai
A. Jacob
Peter A. Beerel
Akhilesh R. Jaiswal
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Improving the Energy Efficiency and Robustness of tinyML Computer Vision using Log-Gradient Input Images
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B. Murmann
247
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Dynamic Iterative Refinement for Efficient 3D Hand Pose Estimation
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Brandon Smart
Simyung Chang
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Nojun Kwak
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MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
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ConformalLayers: A non-linear sequential neural network with associative layers
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Network Augmentation for Tiny Deep Learning
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SIRNN: A Math Library for Secure RNN Inference
IEEE Symposium on Security and Privacy (IEEE S&P), 2021
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Mayank Rathee
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Aseem Rastogi
193
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Going Deeper Into Face Detection: A Survey
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Kevin W. Bowyer
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Implicit Bias of Linear RNNs
International Conference on Machine Learning (ICML), 2021
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Mojtaba Sahraee-Ardakan
Parthe Pandit
S. Rangan
A. Fletcher
203
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TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning
Han Cai
Chuang Gan
Ligeng Zhu
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288
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One Size Does Not Fit All: Multi-Scale, Cascaded RNNs for Radar Classification
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S. Srivastava
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