SearcHD: A Memory-Centric Hyperdimensional Computing With Stochastic Training
Abstract
Brain-inspired hyperdimensional (HD) computing emulates cognitive tasks by computing with long binary vectors-also know as hypervectors-as opposed to computing with numbers. However, we observed that in order to provide acceptable classification accuracy on practical applications, HD algorithms need to be trained and tested on nonbinary hypervectors. In this article, we propose SearcHD, a fully binarized HD computing algorithm with a fully binary training. SearcHD maps every data points to a high-dimensional space with binary elements. Instead of training an HD model with nonbinary elements, SearcHD implements a full binary training method which generates multiple binary hypervectors for each class. We also use the analog characteristic of nonvolatile memories (NVMs) to perform all encoding, training, and inference computations in memory. We evaluate the efficiency and accuracy of SearcHD on a wide range of classification applications. Our evaluation shows that SearcHD can provide on average 31.1× higher energy efficiency and 12.8× faster training as compared to the state-of-the-art HD computing algorithms.
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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 [141]
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Liu, L., Kumar, S., Thomann, S., Amrouch, H., & Hu, X. S. (2023) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac56929.2023.10247849 · Cited by 9 · see citing works
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Yan, Z., Wang, S., Tang, K., & Wong, W. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43415-0_9 · Cited by 9 · see citing works
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Farzaneh, H., de Lima, J. P. C., Li, M., Khan, A. A., Hu, X. S., & Castrillón, J. (2024) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3620666.3651386 · Cited by 9 · see citing works
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Li, T., Zhong, H., Wu, J., Kämpfe, T., Ni, K., Narayanan, V., Yang, H., & Li, X. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546635 · Cited by 8 · see citing works
Tri-HD: Energy-Efficient On-Chip Learning With In-Memory Hyperdimensional Computing
Xu, W., Gupta, S., Morris, J., Shen, X., Imani, M., Akşanlı, B., & Rosing, T. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3435679 · Cited by 7 · see citing works
Self-citationAdaptBit-HD: Adaptive Model Bitwidth for Hyperdimensional Computing
Morris, J., Set, S. T. K., Rosen, G., Imani, M., Akşanlı, B., & Rosing, T. (2021) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd53106.2021.00026 · Cited by 7 · see citing works
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Cai, J., Barkam, H. E., Imani, M., Ni, K., Zhang, G. L., Li, B., Schlichtmann, U., Zhuo, C., & Yin, X. (2024) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2024.3493000 · Cited by 6 · see citing works
Self-citationHDCOG: A Lightweight Hyperdimensional Computing Framework with Feature Extraction
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IP-HDC: Information-Preserved Hyperdimensional Computing for Multi-Task Learning
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EnHDC: Ensemble Learning for Brain-Inspired Hyperdimensional Computing
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TAP-CAM: A Tunable Approximate Matching Engine based on Ferroelectric Content Addressable Memory
Ni, C., Chen, S., Liu, C.-K., Liu, L., Imani, M., Kämpfe, T., Ni, K., Niemier, M. T., Hu, X. S., Zhuo, C., et al. (2024) · doi:10.1145/3676536.3676699 · Cited by 5 · see citing works
Self-citationLi, H., Liu, F., Chen, Y., Wang, Z., Huang, S., Yang, N., Lyu, D., & Jiang, L. (2025) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3695053.3731031 · Cited by 5 · see citing works
In-Memory Acceleration of Hyperdimensional Genome Matching on Unreliable Emerging Technologies
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Self-citationPAAP-HD: PIM-Assisted Approximation for Efficient Hyper-Dimensional Computing
Liu, F., Li, H., Yang, N., Chen, Y., Wang, Z., Yang, T., & Jiang, L. (2024) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac58780.2024.10473823 · Cited by 5 · see citing works
Stochastic-HD: Leveraging Stochastic Computing on Hyper-Dimensional Computing
Hao, Y., Gupta, S., Morris, J., Khaleghi, B., Akşanlı, B., & Rosing, T. (2021) · IEEE International Conference on Computer Design: VLSI in Computers and Processors (ICCD) · doi:10.1109/iccd53106.2021.00058 · Cited by 4 · see citing works
Self-citationInvited Paper: Algorithm/Hardware Co-Design for Few-Shot Learning at the Edge
Laguna, A. F., Sharifi, M. M., Reis, D. A., Liu, L., Hennessee, A., O'Dell, C., O’Connor, I., Niemier, M. T., & Hu, X. S. (2023) · doi:10.1109/iccad57390.2023.10323933 · Cited by 3 · see citing works
Self-citationSuperconducting Hyperdimensional Associative Memory Circuit for Scalable Machine Learning
Huch, K., Gonzalez-Guerrero, P., Lyles, D., & Michelogiannakis, G. (2023) · IEEE Transactions on Applied Superconductivity · doi:10.1109/tasc.2023.3271951 · Cited by 3 · see citing works
SenHDC: A 3-D NAND Flash-Based Processing-in-Sensor Hyperdimensional Computing Architecture
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Self-citationStochastic-HD: Leveraging Stochastic Computing on the Hyper-Dimensional Computing Pipeline
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Sapui, B., & Tahoori, M. B. (2025) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1145/3658617.3697768 · Cited by 2 · see citing works
Cross-layer Design for Computing-in-Memory
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Self-citationClassification Performance of Confidence-Driven Centroids
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Performance Comparison of Different Classifiers Applied to Gesture Recognition from sEMG Signals
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TransHD: Spatial Transformer Features Extraction for HDC Synergetic Learning
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Branch Predicting with Sparse Distributed MemoriesPreprint
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2020 Index IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems Vol. 39
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Machine Learning in IoT Systems: From Deep Learning to Hyperdimensional Computing
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Self-citationAnalog Content Addressable Memory using Ferroelectric: A Case Study of Search-in-Memory
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Energy-Efficient Large-Scale Vector Similarity Search in NAND-Flash via Hybrid Matching
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Energy-Efficient In-Memory Vector Similarity Search With Fidelity-Aware Range Encoding
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PhotoHDC: An Electro-Photonic Accelerator for Hyperdimensional Computing
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Self-citationPrimitive-Driven Acceleration of Hyperdimensional Computing for Real-Time Image Classification
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Increasing the Efficiency of Associative Processor Architectures via CMOS-Compatible Hybridization
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Multicentroid Hyperdimensional Computing for Compact IMC Arrays via Dimension Pruning
Kang, D. Y., Oh, Y., Hwang, C., Kim, J., Jeon, K. E., & Ko, J. H. (2026) · IEEE Internet of Things Journal · doi:10.1109/jiot.2026.3730047
BHDC: Efficient Hyperdimensional Computing under Feature-to-Dimension Imbalance
Saeidi, A., Verreault, A., Brun, D., & Robichaud, A. (2026) · IEEE Interregional NEWCAS Conference (NEWCAS) · doi:10.1109/newcas64543.2026.11673999
- Authors
- Imani, M., Yin, X., Messerly, J., Gupta, S., Niemier, M., Hu, X. S., & Rosing, T.
- Affiliations
- University of California San Diego (US)
- University of Notre Dame (US)
- Venue
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
- Published
- 2020-10
- Volume
- 39
- Issue
- 10
- Pages
- 2422-2433
- ISSN
- 0278-0070 (ISSN-L)
- 1937-4151
- OpenAlex
- W2983032988
- Cited by
- 87
- References
- 54
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 16 · 2023: 10 · 2022: 14 · 2021: 19 · 2020: 8 · 2019: 1
- FWCI
- 3.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (publishedVersion)
- Funders
- CRISP and ASCENT, two of six centers in JUMP, an SRC program
- DARPA
- NSF (1911095, 1826967, 1730158, 1527034)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Hardware and Architecture › Network Packet Processing and Optimization
- Keywords
- hyperdimensional computing, hypervectors, memory-centric computing, non-volatile memory, stochastic training, neuromorphic computing, energy efficiency, classification, in-memory computing
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