The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Energy-efficient Mott activation neuron for full-hardware implementation of neural networks
Open accessAbstract
To circumvent the von Neumann bottleneck, substantial progress has been made towards in-memory computing with synaptic devices. However, compact nanodevices implementing non-linear activation functions are required for efficient full-hardware implementation of deep neural networks. Here, we present an energy-efficient and compact Mott activation neuron based on vanadium dioxide and its successful integration with a conductive bridge random access memory (CBRAM) crossbar array in hardware. The Mott activation neuron implements the rectified linear unit function in the analogue domain. The neuron devices consume substantially less energy and occupy two orders of magnitude smaller area than those of analogue complementary metal–oxide semiconductor implementations. The LeNet-5 network with Mott activation neurons achieves 98.38% accuracy on the MNIST dataset, close to the ideal software accuracy. We perform large-scale image edge detection using the Mott activation neurons integrated with a CBRAM crossbar array. Our findings provide a solution towards large-scale, highly parallel and energy-efficient in-memory computing systems for neural networks. Energy- and area-efficient vanadium-dioxide-based Mott activation neuron devices enable the implementation of activation functions in neural networks.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Gradient-based learning applied to document recognition
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Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays
Shi, Y., Nguyen, L., Oh, S., Liu, X., Koushan, F., Jameson, J. R., & Kuzum, D. (2018) · Nature Communications · doi:10.1038/s41467-018-07682-0 · Cited by 106
Self-citationDesign Considerations for Efficient Deep Neural Networks on Processing-in-Memory Accelerators
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“This is done by the so-called neuron activation functions, and the most common are: Sigmoid (also called Logistic) 119,120 , Hyperbolic Tangent 120 and Recti fi ed Linear Unit (ReLU) 120,121 .”
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2022 roadmap on neuromorphic computing and engineering
Christensen, D. V., Dittmann, R., Linares-Barranco, B., Sebastian, A., Le Gallo, M., Redaelli, A., Slesazeck, S., Mikolajick, T., Spiga, S., Menzel, S., et al. (2022) · Neuromorphic Computing and Engineering · doi:10.1088/2634-4386/ac4a83
Metadata
- Authors
- Oh, S., Shi, Y., del Valle, J., Salev, P., Lu, Y., Huang, Z., Kalcheim, Y., Schuller, I. K., & Kuzum, D.
- Venue
- Nature Nanotechnology
- Published
- 2021-03-18
- Volume
- 16
- Issue
- 6
- Pages
- 680-687
- ISSN
- 1748-3387 (ISSN-L)
- 1748-3395
- OpenAlex
- W3136154048
- Cited by
- 161
- References
- 5
- FWCI
- 7.56
- Type
- journal article
- Language
- en
- Open access
- green
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- Oh, S., Shi, Y., del Valle, J., Salev, P., Lu, Y., Huang, Z., Kalcheim, Y., Schuller, I. K., & Kuzum, D. (2021). Energy-efficient Mott activation neuron for full-hardware implementation of neural networks. Nature Nanotechnology. https://doi.org/10.1038/s41565-021-00874-8
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