Fetching the paper…

Reading the bibliography…

2018

Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware

Blouw, Peter, Choo, Xuan, Hunsberger, Eric et al.

Original
PDF
Post on X

Understand

Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit, we analyze the inference speed, dynamic power consumption, and energy cost per inference of a two-layer neural network keyword spotter trained to recognize a single phrase.

We perform comparative analyses of this keyword spotter running on more conventional hardware devices including a CPU, a GPU, Nvidia's Jetson TX1, and the Movidius Neural Compute Stick.
Our results indicate that for this inference application, Loihi outperforms all of these alternatives on an energy cost per inference basis while maintaining equivalent inference accuracy.
Furthermore, an analysis of tradeoffs between network size, inference speed, and energy cost indicates that Loihi's comparative advantage over other low-power computing devices improves for larger networks.

Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware · Around

Built on

Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks
Graves, A., Fernandez, S., Gomez, F., and Schmidhuber, J · 2006
Earlier work this paper cites.
Nengo: a python tool for building large-scale functional brain models
Bekolay, T., Bergstra, J., Hunsberger, E., DeWolf, T., Stewart, T., Rasmussen, D., Choo, X., Voelker, A., and Eliasmith, C · 2014
Earlier work this paper cites.
Small-footprint keyword spotting using deep neural networks
Chen, G., Parada, C., and Heigold, G · 2014
Earlier work this paper cites.

Similar

Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., and Zhang, X · 2016
Cited alongside, same era.
Training spiking deep networks for neuromorphic hardware
Original
Hunsberger, E. and Eliasmith, C · 2016
Cited alongside, same era.

Then

Loihi: a neuromorphic manycore processor with on-chip learning
Davies, M., Srinivasa, N., Lin, T., Chinya, G., Cao, Y., Choday, S., Dimou, G., Joshi, P., Imam, N., Jain, S., Liao, Y., Lin, C., Lines, A., Liu, R., Mathaikutty, D., McCoy, S., Paul, A., Tse, J., Venkataramanan, G., Weng, Y., Wild, A., Yang, Y., and Wang, H · 2018
Closest in time.
Nengo DL: Combining deep learning and neuromorphic modelling methods
Original
Rasmussen, Daniel · 2018
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…