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Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power)
Open accessAbstract
We review our work towards achieving competitive performance (classification accuracies) for on-chip machine learning (ML) of large-scale artificial neural networks (ANN) using Non-Volatile Memory (NVM)-based synapses, despite the inherent random and deterministic imperfections of such devices. We then show that such systems could potentially offer faster (up to 25×) and lower-power (from 120-2850×) ML training than GPU-based hardware.
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Burr, G. W., BrightSky, M. J., Sebastian, A., Cheng, H.-Y., Wu, J.-Y., Kim, S., Sosa, N. E., Papandreou, N., Lung, H.-L., Pozidis, H., et al. (2016) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems · doi:10.1109/jetcas.2016.2547718 · Cited by 393
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AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed
Chang, H.-Y., Narayanan, P., Lewis, S. C., Farinha, N. C. P., Hosokawa, K., Mackin, C., Tsai, H., Ambrogio, S., Chen, A., & Burr, G. W. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2934050 · Cited by 62
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Metadata
- Authors
- Burr, G. W., Narayanan, P., Shelby, R. M., Sidler, S., Boybat, I., di Nolfo, C., & Leblebici, Y.
- Venue
- 2015 IEEE International Electron Devices Meeting (IEDM)
- Publisher
- IEEE
- Published
- 2015-12
- Pages
- 4.4.1-4.4.4
- ISSN
- 0163-1918 (ISSN-L)
- OpenAlex
- W2248832573
- Cited by
- 168
- References
- 1
- FWCI
- 21.11
- Type
- conference paper
- Language
- en
- Open access
- green
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- The paper
- Burr, G. W., Narayanan, P., Shelby, R. M., Sidler, S., Boybat, I., di Nolfo, C., & Leblebici, Y. (2015). Large-scale neural networks implemented with non-volatile memory as the synaptic weight element: Comparative performance analysis (accuracy, speed, and power). 2015 IEEE International Electron Devices Meeting (IEDM). https://doi.org/10.1109/iedm.2015.7409625
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