The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication
Abstract
Vector-matrix multiplication dominates the computation time and energy for many workloads, particularly neural network algorithms and linear transforms (e.g, the Discrete Fourier Transform). Utilizing the natural current accumulation feature of memristor crossbar, we developed the Dot-Product Engine (DPE) as a high density, high power efficiency accelerator for approximate matrix-vector multiplication. We firstly invented a conversion algorithm to map arbitrary matrix values appropriately to memristor conductances in a realistic crossbar array, accounting for device physics and circuit issues to reduce computational errors. The accurate device resistance programming in large arrays is enabled by close-loop pulse tuning and access transistors. To validate our approach, we simulated and benchmarked one of the state-of-the-art neural networks for pattern recognition on the DPEs. The result shows no accuracy degradation compared to software approach (99 % pattern recognition accuracy for MNIST data set) with only 4 Bit DAC/ADC requirement, while the DPE can achieve a speed-efficiency product of 1,000× to 10,000× compared to a custom digital ASIC.
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Citations
Cites
Nanoscale Memristor Device as Synapse in Neuromorphic Systems
Jo, S. H., Chang, T., Ebong, I., Bhadviya, B. B., Mazumder, P., & Lu, W. (2010) · Nano Letters · doi:10.1021/nl904092h · Cited by 4,174 · cited 2×
“For analog computing applications, memristors are desired to have stable, continuous linear conductance states for representing matrix values [13].”
BackgroundMemristive devices for computing
Yang, J. J., Strukov, D. B., & Stewart, D. R. (2013) · Nature Nanotechnology · doi:10.1038/nnano.2012.240 · Cited by 3,885
Self-citationTraining and operation of an integrated neuromorphic network based on metal-oxide memristors
Prezioso, M., Merrikh-Bayat, F., Hoskins, B. D., Adam, G. C., Likharev, K. K., & Strukov, D. B. (2015) · Nature · doi:10.1038/nature14441 · Cited by 3,001
High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm
Alibart, F., Gao, L., Hoskins, B. D., & Strukov, D. B. (2012) · Nanotechnology · doi:10.1088/0957-4484/23/7/075201 · Cited by 557 · cited 1×
“After the conversion, we use close-loop tuning scheme [16]to program memristors to the desired conductance values.”
MethodData Clustering using Memristor Networks
Choi, S., Sheridan, P., & Lu, W. D. (2015) · Scientific Reports · doi:10.1038/srep10492 · Cited by 132
Cited by
The future of electronics based on memristive systems
Zidan, M. A., Strachan, J. P., & Lu, W. D. (2018) · Nature Electronics · doi:10.1038/s41928-017-0006-8 · Cited by 2,138
Self-citationParallel convolutional processing using an integrated photonic tensor core
Feldmann, J., Youngblood, N., Karpov, M., Gehring, H., Li, X., Stappers, M., Le Gallo, M., Fu, X., Lukashchuk, A., Raja, A. S., et al. (2021) · Nature · doi:10.1038/s41586-020-03070-1 · Cited by 1,900
Neuro-Inspired Computing With Emerging Nonvolatile Memorys
Yu, S. (2018) · Proceedings of the IEEE · doi:10.1109/jproc.2018.2790840 · Cited by 1,170
Equivalent-accuracy accelerated neural-network training using analogue memory
Ambrogio, S., Narayanan, P., Tsai, H., Shelby, R. M., Boybat, I., di Nolfo, C., Sidler, S., Giordano, M., Bodini, M., Farinha, N. C. P., et al. (2018) · Nature · doi:10.1038/s41586-018-0180-5 · Cited by 1,151
Neuro-inspired computing chips
Zhang, W., Gao, B., Tang, J., Yao, P., Yu, S., Chang, M.-F., Yoo, H.-J., Qian, H., & Wu, H. (2020) · Nature Electronics · doi:10.1038/s41928-020-0435-7 · Cited by 892
Metadata
- Authors
- Hu, M., Strachan, J. P., Li, Z., Grafals, E. M., Davila, N., Graves, C., Lam, S., Ge, N., Yang, J. J., & Williams, R. S.
- Venue
- Proceedings of the 53rd Annual Design Automation Conference
- Publisher
- ACM
- Published
- 2016-06-05
- Pages
- 1-6
- ISSN
- 0738-100X (ISSN-L)
- OpenAlex
- W2399958287
- Cited by
- 686
- References
- 5
- FWCI
- 99.88
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hu, M., Strachan, J. P., Li, Z., Grafals, E. M., Davila, N., Graves, C., Lam, S., Ge, N., Yang, J. J., & Williams, R. S. (2016). Dot-product engine for neuromorphic computing: programming 1T1M crossbar to accelerate matrix-vector multiplication. Proceedings of the 53rd Annual Design Automation Conference. https://doi.org/10.1145/2897937.2898010
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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