Fetching the paper…
Reading the bibliography…
Recent advancements in ultra-low-power machine learning (TinyML) hardware promises to unlock an entirely new class of smart applications.
Chowdhery, A., Warden, P., Shlens, J., Howard, A., and Rhodes, R · 1906
Earlier work this paper cites.
PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals
Goldberger, A. L., Amaral, L. A. N., Glass, L., Hausdorff, J. M., Ivanov, P. C., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C.-K., and Stanley, H. E · 2000
Earlier work this paper cites.
Koizumi, Y., Kawaguchi, Y., Imoto, K., Nakamura, T., Nikaido, Y., Tanabe, R., Purohit, H., Suefusa, K., Endo, T., Yasuda, M., and Harada, N · 2006
Earlier work this paper cites.
On field calibration of an electronic nose for benzene estimation in an urban pollution monitoring scenario
De Vito, S., Massera, E., Piga, M., and Martinotto, L · 2007
Earlier work this paper cites.
Turbofan engine degradation simulation data set, 2008
Saxena, A. and Goebel, K · 2008
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Comparative study on classifying human activities with miniature inertial and magnetic sensors
Altun, K., Barshan, B., and Tunçel, O · 2010
Earlier work this paper cites.
MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
Earlier work this paper cites.
Collecting complex activity datasets in highly rich networked sensor environments
Roggen, D., Calatroni, A., Rossi, M., Holleczek, T., Förster, K., Tröster, G., Lukowicz, P., Bannach, D., Pirkl, G., Ferscha, A., Doppler, J., Holzmann, C., Kurz, M., Holl, G., Chavarriaga, R., Sagha, H., Bayati, H., Creatura, M., and d. R. Millàn, J · 2010
Earlier work this paper cites.
Chemical gas sensor drift compensation using classifier ensembles
Vergara, A., Vembu, S., Ayhan, T., Ryan, M., Homer, M., and Huerta, R · 2012
Earlier work this paper cites.
Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
Earlier work this paper cites.
A low power, fully event-based gesture recognition system
Amir, A., Taba, B., Berg, D., Melano, T., McKinstry, J., Nolfo, C. D., Nayak, T., Andreopoulos, A., Garreau, G., Mendoza, M., Kusnitz, J., Debole, M., Esser, S., Delbruck, T., Flickner, M., and Modha, D · 2017
Cited alongside, same era.
Audio set: An ontology and human-labeled dataset for audio events
Gemmeke, J. F., Ellis, D. P. W., Freedman, D., Jansen, A., Lawrence, W., Moore, R. C., Plakal, M., and Ritter, M · 2017
Cited alongside, same era.
Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
Cited alongside, same era.
Resource-efficient machine learning in 2 KB RAM for the internet of things
Kumar, A., Goyal, S., and Varma, M · 2017
Cited alongside, same era.
Recognizing detailed human context in the wild from smartphones and smartwatches
Vaizman, Y., Ellis, K., and Lanckriet, G · 2017
Cited alongside, same era.
why the future of machine learning is tiny, 2018b
Warden, P · 2018
Later among the works it cites.
Precis har, 2019
Cramariuc, A.-C. P. I. M. B · 2019
Later among the works it cites.
Sparse: Sparse architecture search for cnns on resource-constrained microcontrollers
Fedorov, I., Adams, R. P., Mattina, M., and Whatmough, P · 2019
Later among the works it cites.
Pulp-nn: accelerating quantized neural networks on parallel ultra-low-power risc-v processors
Garofalo, A., Rusci, M., Conti, F., Rossi, D., and Benini, L · 2019
Later among the works it cites.
The speed and power advantage of a purpose-built neural compute engine, Jun 2019
Holleman, J · 2019
Later among the works it cites.
A 1-16b precision reconfigurable digital in-memory computing macro featuring column-mac architecture and bit-serial computation
Kim, H., Chen, Q., Yoo, T., Kim, T. T.-H., and Kim, B · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hello edge: Keyword spotting on microcontrollers, 2017
Zhang, Y., Suda, N., Lai, L., and Chandra, V · 2017
Cited alongside, same era.
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
Cited alongside, same era.
A robust human activity recognition system using smartphone sensors and deep learning
Hassan, M. M., Uddin, M. Z., Mohamed, A., and Almogren, A · 2018
Cited alongside, same era.
Cmsis-nn: Efficient neural network kernels for arm cortex-m cpus, 2018
Lai, L., Suda, N., and Chandra, V · 2018
Cited alongside, same era.
Latent factors limiting the performance of semg-interfaces
Lobov, S., Krilova, N., Kastalskiy, I., Kazantsev, V., and Makarov, V · 2018
Cited alongside, same era.
Binareye: An always-on energy-accuracy-scalable binary cnn processor with all memory on chip in 28nm cmos
Moons, B., Bankman, D., Yang, L., Murmann, B., and Verhelst, M · 2018
Cited alongside, same era.
URL https://www.arm.com/why-arm/technologies/helium
Helium: Enhancing the capabilities of the smallest devices
Cited in the paper.
Later among the works it cites.
Toyadmos: A dataset of miniature-machine operating sounds for anomalous sound detection
Koizumi, Y., Saito, S., Uematsu, H., Harada, N., and Imoto, K · 2019
Later among the works it cites.
Mlperf inference benchmark, 2019
Reddi, V. J., Cheng, C., Kanter, D., Mattson, P., Schmuelling, G., Wu, C.-J., Anderson, B., Breughe, M., Charlebois, M., Chou, W., Chukka, R., Coleman, C., Davis, S., Deng, P., Diamos, G., Duke, J., Fick, D., Gardner, J. S., Hubara, I., Idgunji, S., Jablin, T. B., Jiao, J., John, T. S., Kanwar, P., Lee, D., Liao, J., Lokhmotov, A., Massa, F., Meng, P., Micikevicius, P., Osborne, C., Pekhimenko, G., Rajan, A. T. R., Sequeira, D., Sirasao, A., Sun, F., Tang, H., Thomson, M., Wei, F., Wu, E., Xu, L., Yamada, K., Yu, B., Yuan, G., Zhong, A., Zhang, P., and Zhou, Y · 2019
Later among the works it cites.
tinyml summit, 2019
tinyML Foundation · 2019
Later among the works it cites.
Haq: Hardware-aware automated quantization with mixed precision
Wang, K., Liu, Z., Lin, Y., Lin, J., and Han, S · 2019
Later among the works it cites.