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IoT devices are powered by microcontroller units (MCUs) which are extremely resource-scarce: a typical MCU may have an underpowered processor and around 64 KB of memory and persistent storage, which is orders of magnitude fewer computational resources than is typically required for deep learning.
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Krizhevsky, A · 2009
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Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and < < 0.5 MB model size
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Neural architecture search with reinforcement learning
Zoph, B. and Le, Q. V · 2016
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A survey of model compression and acceleration for deep neural networks
Cheng, Y., Wang, D., Zhou, P., and Zhang, T · 2017
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ProtoNN: Compressed and accurate knn for resource-scarce devices
Gupta, C., Suggala, A. S., Goyal, A., Simhadri, H. V., Paranjape, B., Kumar, A., Goyal, S., Udupa, R., Varma, M., and Jain, P · 2017
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Resource-efficient machine learning in 2 kb ram for the internet of things
Kumar, A., Goyal, S., and Varma, M · 2017
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Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Hello Edge: Keyword spotting on microcontrollers
Zhang, Y., Suda, N., Lai, L., and Chandra, V · 2017
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ProxylessNAS: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 2018
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Efficient multi-objective neural architecture search via Lamarckian evolution
Elsken, T., Metzen, J. H., and Hutter, F · 2018
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Bohb: Robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F · 2018
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MONAS: Multi-objective neural architecture search using reinforcement learning
Hsu, C.-H., Chang, S.-H., Liang, J.-H., Chou, H.-P., Liu, C.-H., Chang, S.-C., Pan, J.-Y., Chen, Y.-T., Wei, W., and Juan, D.-C · 2018
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Quantization and training of neural networks for efficient integer-arithmetic-only inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., and Kalenichenko, D · 2018
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DARTS: Differentiable architecture search
Liu, H., Simonyan, K., and Yang, Y · 2018
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Efficient neural architecture search via parameters sharing
Pham, H., Guan, M., Zoph, B., Le, Q., and Dean, J · 2018
Neural networks on microcontrollers: saving memory at inference via operator reordering
Liberis, E. and Lane, N. D · 2019
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AtomNas: Fine-grained end-to-end neural architecture search
Mei, J., Li, Y., Lian, X., Jin, X., Yang, L., Yuille, A., and Yang, J · 2019
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CoopNet: Cooperative convolutional neural network for low-power MCUs
Mocerino, L. and Calimera, A · 2019
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Regularized evolution for image classifier architecture search
Real, E., Aggarwal, A., Huang, Y., and Le, Q. V · 2019
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MNasNet: Platform-aware neural architecture search for mobile
Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., and Le, Q. V · 2019
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MobileNet V2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
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Speech commands: A dataset for limited-vocabulary speech recognition
Warden, P · 2018
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SNAS: stochastic neural architecture search
Xie, S., Zheng, H., Liu, C., and Lin, L · 2018
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Resource-efficient neural architect
Zhou, Y., Ebrahimi, S., Arık, S. Ö., Yu, H., Liu, H., and Diamos, G · 2018
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Cheng, H.-P., Zhang, T., Yang, Y., Yan, F., Li, S., Teague, H., Li, H., and Chen, Y · 2019
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Chowdhery, A., Warden, P., Shlens, J., Howard, A., and Rhodes, R · 2019
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Neural architecture search: A survey
Elsken, T., Metzen, J. H., and Hutter, F · 2019
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TinyML—How TVM is Taming Tiny
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Microcontroller Market Size, Share & Trends Analysis Report By Product (8-bit, 16-bit, 32-bit), By Application (Automotive, Consumer Electronics, Industrial, Medical Devices, Military & Defense), And Segment Forecasts, 2020–2027
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