Fetching the paper…
Reading the bibliography…
Executing machine learning workloads locally on resource constrained microcontrollers (MCUs) promises to drastically expand the application space of IoT.
Temporal convolution for real-time keyword spotting on mobile devices
Choi, S., Seo, S., Shin, B., Byun, H., Kersner, M., Kim, B., Kim, D., and Ha, S · 1904
Earlier work this paper cites.
Compressing rnns for iot devices by 15-38x using kronecker products
Thakker, U., Beu, J. G., Gope, D., Zhou, C., Fedorov, I., Dasika, G., and Mattina, M · 1906
Earlier work this paper cites.
Pushing the limits of RNN compression
Thakker, U., Fedorov, I., Beu, J. G., Gope, D., Zhou, C., Dasika, G., and Mattina, M · 1910
Earlier work this paper cites.
PROFIT: A novel training method for sub-4-bit mobilenet models
Park, E. and Yoo, S · 2008
Earlier work this paper cites.
Leveraging automated mixed-low-precision quantization for tiny edge microcontrollers
Rusci, M., Fariselli, M., Capotondi, A., and Benini, L · 2008
Earlier work this paper cites.
Computer Architecture, Fifth Edition: A Quantitative Approach
Hennessy, J. L. and Patterson, D. A · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
Earlier work this paper cites.
Distilling the knowledge in a neural network
Hinton, G., Vinyals, O., and Dean, J · 2015
Earlier work this paper cites.
High performance dsp for vision, imaging and neural networks
Efland, G., Parikh, S., Sanghavi, H., and Farooqui, A · 2016
Earlier work this paper cites.
Protonn: Compressed and accurate knn for resource-scarce devices
Gupta, A., Suggala, C. G. S., Gupta, A., Simhadri, H., Paranjape, B., Kumar, A., Goyal, S., Udupa, R., Varma, M., and Jain, P · 2017
Earlier work this paper cites.
Applications of Deep Neural Networks for Ultra Low Power IoT
Kodali, S., Hansen, P., Mulholland, N., Whatmough, P., Brooks, D., and Wei, G · 2017
Earlier work this paper cites.
Resource-efficient machine learning in 2 kb ram for the internet of things
Kumar, A., Goyal, S., and Varma, M · 2017
Earlier work this paper cites.
Gap-8: A risc-v soc for ai at the edge of the iot
Flamand, E., Rossi, D., Conti, F., Loi, I., Pullini, A., Rotenberg, F., and Benini, L · 2018
Earlier work this paper cites.
Quantizing deep convolutional networks for efficient inference: A whitepaper
Krishnamoorthi, R · 2018
Earlier work this paper cites.
Fastgrnn: A fast, accurate, stable and tiny kilobyte sized gated recurrent neural network
Kusupati, A., Singh, M., Bhatia, K., Kumar, A., Jain, P., and Varma, M · 2018
Earlier work this paper cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Earlier work this paper cites.
Speech commands: A dataset for limited-vocabulary speech recognition
Warden, P · 2018
Earlier work this paper cites.
DNN Engine: A 28-nm Timing-Error Tolerant Sparse Deep Neural Network Processor for IoT Applications
Whatmough, P. N., Lee, S. K., Brooks, D., and Wei, G · 2018
Cited alongside, same era.
ProxylessNAS: Direct neural architecture search on target task and hardware
Cai, H., Zhu, L., and Han, S · 2019
Cited alongside, same era.
Small-footprint keyword spotting with graph convolutional network, 2019
Chen, X., Yin, S., Song, D., Ouyang, P., Liu, L., and Wei, S · 2019
Cited alongside, same era.
Msnet: Structural wired neural architecture search for internet of things
Cheng, H.-P., Zhang, T., Yang, Y., Yan, F., Teague, H., Chen, Y., and Li, H · 2019
Cited alongside, same era.
Chowdhery, A., Warden, P., Shlens, J., Howard, A., and Rhodes, R · 2019
Cited alongside, same era.
Performance-oriented neural architecture search, 2020
Anderson, A., Su, J., Dahyot, R., and Gregg, D · 2020
Closest in time.
Benchmarking tinyml systems: Challenges and direction
Banbury, C. R., Reddi, V. J., Lam, M., Fu, W., Fazel, A., Holleman, J., Huang, X., Hurtado, R., Kanter, D., Lokhmotov, A., et al · 2020
Closest in time.
Cmix-nn: Mixed low-precision cnn library for memory-constrained edge devices
Capotondi, A., Rusci, M., Fariselli, M., and Benini, L · 2020
Closest in time.
Tensorflow lite micro: Embedded machine learning on tinyml systems
David, R., Duke, J., Jain, A., Reddi, V. J., Jeffries, N., Li, J., Kreeger, N., Nappier, I., Natraj, M., Regev, S., et al · 2020
Closest in time.
Learned step size quantization
Esser, S. K., McKinstry, J. L., Bablani, D., Appuswamy, R., and Modha, D. S · 2020
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Network pruning via transformable architecture search
Dong, X. and Yang, Y · 2019
Cited alongside, same era.
Neural architecture search: A survey
Elsken, T., Metzen, J. H., and Hutter, F · 2019
Cited alongside, same era.
SpArSe: Sparse architecture search for cnns on resource-constrained microcontrollers
Fedorov, I., Adams, R. P., Mattina, M., and Whatmough, P · 2019
Cited alongside, same era.
Ternary hybrid neural-tree networks for highly constrained iot applications
Gope, D., Dasika, G., and Mattina, M · 2019
Cited alongside, same era.
Memory-optimal direct convolutions for maximizing classification accuracy in embedded applications
Gural, A. and Murmann, B · 2019
Cited alongside, same era.
Using self-supervised learning can improve model robustness and uncertainty
Hendrycks, D., Mazeika, M., Kadavath, S., and Song, D · 2019
Cited alongside, same era.
On-Chip Memory Technology Design Space Explorations for Mobile Deep Neural Network Accelerators
Li, H., Bhargav, M., Whatmough, P. N., and Philip Wong, H. · 2019
Cited alongside, same era.
TinyLSTMs: Efficient Neural Speech Enhancement for Hearing Aids
Fedorov, I., Stamenovic, M., Jensen, C., Yang, L.-C., Mandell, A., Gan, Y., Mattina, M., and Whatmough, P. N · 2020
Closest in time.
Unsupervised anomalous sound detection using self-supervised classification and group masked autoencoder for density estimation
Giri, R., Tenneti, S. V., Helwani, K., Cheng, F., Isik, U., and Krishnaswamy, A · 2020
Closest in time.
Ternary mobilenets via per-layer hybrid filter banks
Gope, D., Beu, J., Thakker, U., and Mattina, M · 2020
Closest in time.
Pushing the envelope of dynamic spatial gating technologies
Huang, X., Thakker, U., Gope, D., and Beu, J · 2020
Closest in time.
Mcunet: Tiny deep learning on iot devices
Lin, J., Chen, W.-M., Lin, Y., Cohn, J., Gan, C., and Han, S · 2020
Closest in time.
Neural architecture search for keyword spotting
Mo, T., Yu, Y., Salameh, M., Niu, D., and Jui, S · 2020
Closest in time.
Ribeiro, A., Matos, L. M., Pereira, P. J., Nunes, E. C., Ferreira, A. L., Cortez, P., and Pilastri, A · 2020
Closest in time.
Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions
Wan, A., Dai, X., Zhang, P., He, Z., Tian, Y., Xie, S., Wu, B., Yu, M., Xu, T., Chen, K., Vajda, P., and Gonzalez, J. E · 2020
Closest in time.
Tinyspeech: Attention condensers for deep speech recognition neural networks on edge devices, 2020
Wong, A., Famouri, M., Pavlova, M., and Surana, S · 2020
Closest in time.
Mobile Machine Learning Hardware at ARM: A Systems-on-Chip (SoC) Perspective
Zhu, Y., Mattina, M., and Whatmough, P. N · 2020
Closest in time.
Thakker, U., Whatmough, P. N., Liu, Z., Mattina, M., and Beu, J · 2021
Closest in time.