Hyperdimensional computing with 3D VRRAM in-memory kernels: Device-architecture co-design for energy-efficient, error-resilient language recognition
Abstract
The ability to learn from few examples, known as one-shot learning, is a hallmark of human cognition. Hyperdimensional (HD) computing is a brain-inspired computational framework capable of one-shot learning, using random binary vectors with high dimensionality. Device-architecture co-design of HD cognitive computing systems using 3D VRRAM/CMOS is presented for language recognition. Multiplication-addition-permutation (MAP), the central operations of HD computing, are experimentally demonstrated on 4-layer 3D VRRAM/FinFET as non-volatile in-memory MAP kernels. Extensive cycle-to-cycle (up to 1012cycles) and wafer-level device-to-device (256 RRAMs) experiments are performed to validate reproducibility and robustness. For 28-nm node, the 3D in-memory architecture reduces total energy consumption by 52.2% with 412 times less area compared with LP digital design (using registers as memory), owing to the energy-efficient VRRAM MAP kernels and dense connectivity. Meanwhile, the system trained with 21 samples texts achieves 90.4% accuracy recognizing 21 European languages on 21,000 test sentences. Hard-error analysis shows the HD architecture is amazingly resilient to RRAM endurance failures, making the use of various types of RRAMs/CBRAMs (1k ~ 10M endurance) feasible.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In-memory computing is widely studied as an energy-efficient approach for hyperdimensional computing.
“Therefore, in-memory computing is widely studied as an energy-efficient approach for hyperdimensional computing [135, 140,141,142].”
From Brain-inspired computing systems: a systematic literature review (2024) · cites this paper as [142]
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 · see citing works
Memristive 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 · see citing works
Speed of processing in the human visual system
Thorpe, S., Fize, D., & Marlot, C. (1996) · Nature · doi:10.1038/381520a0 · Cited by 3,538 · see citing works
Training 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 · see citing works
Neuromorphic electronic systems
Mead, C. (1990) · Proceedings of the IEEE · doi:10.1109/5.58356 · Cited by 2,368 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
In-memory hyperdimensional computing
Karunaratne, G., Le Gallo, M., Cherubini, G., Benini, L., Rahimi, A., & Sebastian, A. (2020) · Nature Electronics · doi:10.1038/s41928-020-0410-3 · Cited by 293 · see citing works
Self-citationCMOS-integrated memristive non-volatile computing-in-memory for AI edge processors
Chen, W.-H., Dou, C., Li, K.-X., Lin, W.-Y., Li, P.-Y., Huang, J.-H., Wang, J.-H., Wei, W.-C., Xue, C.-X., Chiu, Y.-C., et al. (2019) · Nature Electronics · doi:10.1038/s41928-019-0288-0 · Cited by 276 · see citing works
Self-citationClassification using hyperdimensional computing: a review
Ge, L., & Parhi, K. K. (2020) · IEEE Circuits and Systems Magazine · doi:10.1109/mcas.2020.2988388 · Cited by 227 · see citing works
Xue, C.-X., Chiu, Y.-C., Liu, T.-W., Huang, T.-Y., Liu, J.-S., Chang, T.-W., Kao, H.-Y., Wang, J.-H., Wei, S.-Y., Lee, C.-Y., et al. (2020) · Nature Electronics · doi:10.1038/s41928-020-00505-5 · Cited by 181 · see citing works
SearcHD: A Memory-Centric Hyperdimensional Computing With Stochastic Training
Imani, M., Yin, X., Messerly, J., Gupta, S., Niemier, M., Hu, X. S., & Rosing, T. (2020) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2019.2952544 · Cited by 87 · see citing works
Brain-inspired computing systems: a systematic literature review
Zolfagharinejad, M., Alegre-Ibarra, U., Chen, T., Kinge, S., & van der Wiel, W. G. (2024) · The European Physical Journal B · doi:10.1140/epjb/s10051-024-00703-6 · Cited by 39 · see citing works
“Therefore, in-memory computing is widely studied as an energy-efficient approach for hyperdimensional computing [135, 140–142].”
Method
- Authors
- Li, H., Wu, T. F., Rahimi, A., Li, K.-S., Rusch, M., Lin, C.-H., Hsu, J.-L., Sabry, M. M., Eryilmaz, S. B., Sohn, J., Chiu, W.-C., Chen, M.-C., Wu, T.-T., Shieh, J.-M., Yeh, W.-K., Rabaey, J. M., Mitra, S., & Wong, H.-S. P.
- Affiliations
- Stanford University (US)
- University of California, Berkeley (US)
- ORCID
- Venue
- 2016 IEEE International Electron Devices Meeting (IEDM)
- Publisher
- IEEE
- Published
- 2016-12
- Pages
- 16.1.1-16.1.4
- ISSN
- 0163-1918 (ISSN-L)
- OpenAlex
- W2583857261
- Cited by
- 136
- References
- 5
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 12 · 2023: 14 · 2022: 19 · 2021: 26 · 2020: 10 · 2019: 15 · 2018: 19 · 2017: 9 · 2016: 1
- FWCI
- 20.64
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Technical digest - International Electron Devices Meeting/Technical digest (publishedVersion)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Low-power high-performance VLSI design
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Ferroelectric and Negative Capacitance Devices
- Keywords
- hyperdimensional computing, in-memory computing, one-shot learning, language identification, non-volatile memory, FinFET, energy efficiency, error resilience, neuromorphic computing, cognitive computing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
