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Beyond von Neumann Era: Brain-Inspired Hyperdimensional Computing to the Rescue

Published: 31 January 2023 Publication History

Abstract

Breakthroughs in deep learning (DL) continuously fuel innovations that profoundly improve our daily life. However, DNNs overwhelm conventional computing architectures by their massive data movements between processing and memory units. As a result, novel computer architectures are indispensable to improve or even replace the decades-old von Neumann architecture. Nevertheless, going far beyond the existing von Neumann principles comes with profound reliability challenges for the performed computations. This is due to analog computing together with emerging beyond-CMOS technologies being inherently noisy and inevitably leading to unreliable computing. Hence, novel robust algorithms become a key to go beyond the boundaries of the von Neumann era. Hyper-dimensional Computing (HDC) is rapidly emerging as an attractive alternative to traditional DL and ML algorithms. Unlike conventional DL and ML algorithms, HDC is inherently robust against errors along a much more efficient hardware implementation. In addition to these advantages at hardware level, HDC's promise to learn from little data and the underlying algebra enable new possibilities at the application level. In this work, the robustness of HDC algorithms against errors and beyond von Neumann architectures are discussed. Further, the benefits of HDC as a machine learning algorithm are demonstrated with the example of outlier detection and reinforcement learning.

References

[1]
Charu C Aggarwal and Saket Sathe. 2015. Theoretical foundations and algorithms for outlier ensembles. Acm sigkdd explorations newsletter, 17, 1.
[2]
Hooman Alavizadeh, Hootan Alavizadeh, and Julian Jang-Jaccard. 2022. Deep q-learning based reinforcement learning approach for network intrusion detection. Computers, 11, 3, 41.
[3]
Alon Amid et al. 2020. Chipyard: integrated design, simulation, and implementation framework for custom socs. IEEE Micro, 40, 4, 10--21.
[4]
Richard Bellman. 1952. On the theory of dynamic programming. Proceedings of the National Academy of Sciences of the United States of America, 38, 8, 716.
[5]
Vincent François-Lavet, Peter Henderson, Riashat Islam, Marc G Bellemare, Joelle Pineau, et al. 2018. An introduction to deep reinforcement learning. Foundations and Trends® in Machine Learning, 11, 3--4, 219--354.
[6]
Lulu Ge and Keshab K. Parhi. 2020. Classification using hyperdimensional computing: a review. IEEE Circuits and Systems Magazine, 20, 2, 30--47.
[7]
Paul R Genssler, Hamza E Barkam, Karthik Pandaram, Mohsen Imani, and Hussam Amrouch. 2022. Modeling and predicting transistor aging under workload dependency using machine learning. arXiv preprint arXiv:2207.04134.
[8]
Paul R. Genssler and Hussam Amrouch. 2022. Brain-inspired computing for circuit reliability characterization. IEEE Transactions on Computers.
[9]
Paul R. Genssler and Hussam Amrouch. 2021. Brain-inspired computing for wafer map defect pattern classification. In IEEE International Test Conference.
[10]
Paul R. Genssler, Victor Van Santen, Jörg Henkel, and Hussam Amrouch. 2022. On the reliability of fefet on-chip memory. IEEE Transactions on Computers, 71, 4, 947--958.
[11]
Paul R. Genssler, Austin Vas, and Hussam Amrouch. 2022. Brain-inspired hyperdimensional computing: how thermal-friendly for edge computing? IEEE Embedded Systems Letters.
[12]
Kim Hammar and Rolf Stadler. 2020. Finding effective security strategies through reinforcement learning and Self-Play. In International Conference on Network and Service Management (CNSM 2020) (CNSM 2020). Izmir, Turkey.
[13]
Alejandro Hernandez-Cane, Namiko Matsumoto, Eric Ping, and Mohsen Imani. 2021. Onlinehd: robust, efficient, and single-pass online learning using hyperdimensional system. In 2021 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 56--61.
[14]
Christian Kaiser, Alexander Stocker, and Andreas Festl. 2019. Automotive CAN bus data: An Example Dataset from the AEGIS Big Data Project. (2019).
[15]
Pentti Kanerva. 2009. Hyperdimensional computing: an introduction to computing in distributed representation with high-dimensional random vectors. Cognitive computation, 1, 2.
[16]
Shubham Kumar, Swetaki Chatterjee, Simon Thomann, Paul R. Genssler, Yogesh S. Chauhan, and Hussam Amrouch. 2022. Cross-layer fefet reliability modeling towards robust hyperdimensional computing. In IFIP/IEEE 30th International Conference on Very Large Scale Integration (VLSI-SoC'22).
[17]
Yann LeCun and Corinna Cortes. 2010. MNIST handwritten digit database.
[18]
Dongning Ma et al. 2021. Molehd: automated drug discovery using brain-inspired hyperdimensional computing. (2021).
[19]
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller. 2013. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.
[20]
Yang Ni, Danny Abraham, Mariam Issa, Yeseong Kim, Pietro Mercati, and Mohsen Imani. 2022. Qhd: a brain-inspired hyperdimensional reinforcement learning algorithm. (2022).
[21]
Yang Ni, Mariam Issa, Danny Abraham, Mahdi Imani, Xunzhao Yin, and Mohsen Imani. 2022. Hdpg: hyperdimensional policy-based reinforcement learning for continuous control. In Proceedings of the 59th ACM/IEEE Design Automation Conference (DAC '22). Association for Computing Machinery, 1141--1146.
[22]
Baolin Peng, Xiujun Li, Jianfeng Gao, Jingjing Liu, and Kam-Fai Wong. 2018. Integrating planning for task-completion dialogue policy learning. CoRR, abs/1801.06176.
[23]
Shebuti Rayana. 2016. Outlier detection datasets (odds) library. (2016).
[24]
Saket Sathe and Charu Aggarwal. 2016. Lodes: local density meets spectral outlier detection. In SIAM international conference on data mining.
[25]
Gloria Sepanta. Optimal hardware implementation of hyperdimensional computing. MSc thesis, Shahid Bahonar University of Kerman, (2022).
[26]
Richard S. Sutton. 1990. Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. In Machine Learning Proceedings 1990. Morgan Kaufmann, San Francisco (CA), 216--224.
[27]
Rahul Thapa, Bikal Lamichhane, Dongning Ma, and Xun Jiao. 2021. Spamhd: memory-efficient text spam detection using brain-inspired hyperdimensional computing. In 2021 IEEE Computer Society Annual Symposium on VLSI (ISVLSI).
[28]
Simon Thomann, Paul R Genssler, and Hussam Amrouch. 2022. Hw/sw co-design for reliable in-memory brain-inspired hyperdimensional computing. arXiv preprint arXiv:2202.04789.
[29]
Simon Thomann, Hong Lam Giang Nguyen, Paul R. Genssler, and Hussam Amrouch. 2022. All-in-memory brain-inspired computing using fefet synapses. Frontiers in Electronics, 3.
[30]
Ruixuan Wang, Xun Jiao, and Sharon Hu. 2022. Odhd: one-class brain-inspired hyperdimensional computing for outlier detection. In IEEE/ACM Design Automation Conference (DAC).
[31]
Ruixuan Wang, Fanxin Kong, Hasshi Sudler, and Xun Jiao. 2021. Brief industry paper: hdad: hyperdimensional computing-based anomaly detection for automotive sensor attacks. In 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS). IEEE, 461--464.
[32]
Christopher J. C. H. Watkins and Peter Dayan. 1992. Q-learning. Machine Learning, 8, 3, 279--292.
[33]
Yue Xu, Hyung Gyu Lee, Yujuan Tan, Yu Wu, Xianzhang Chen, Liang Liang, Lei Qiao, and Duo Liu. 2019. Tumbler: energy efficient task scheduling for dual-channelsolar-powered sensor nodes. In 2019 56th ACM/IEEE Design Automation Conference (DAC), 1--6.
[34]
Siyu and others Yue. 2012. Reinforcement learning based dynamic power management with a hybrid power supply. In 2012 IEEE 30th International Conference on Computer Design (ICCD).
[35]
Sizhe Zhang, Mohsen Imani, and Xun Jiao. 2022. Scalehd: robust brain-inspired hyperdimensional computing via adapative scaling. In 2022 IEEE/ACM International Conference on Computer-Aided Design (ICCAD). IEEE.
[36]
Sizhe Zhang, Ruixuan Wang, Dongning Ma, Jeff Jun Zhang, Xunzhao Yin, and Xun Jiao. 2022. Energy-efficient brain-inspired hyperdimensional computing using voltage scaling. In 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 52--55.
[37]
Sizhe Zhang, Ruixuan Wang, Jeff Jun Zhang, Abbas Rahimi, and Xun Jiao. 2021. Assessing robustness of hyperdimensional computing against errors in associative memory. In 2021 IEEE 32nd International Conference on Application-specific Systems, Architectures and Processors (ASAP). IEEE, 211--217.
[38]
Arthur Zimek et al. 2013. Subsampling for efficient and effective unsupervised outlier detection ensembles. In KDD.

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  • (2024)Brain-inspired computing systems: a systematic literature reviewThe European Physical Journal B10.1140/epjb/s10051-024-00703-697:6Online publication date: 6-Jun-2024

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        cover image ACM Conferences
        ASPDAC '23: Proceedings of the 28th Asia and South Pacific Design Automation Conference
        January 2023
        807 pages
        ISBN:9781450397834
        DOI:10.1145/3566097
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        Published: 31 January 2023

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        1. brain-inspired computing
        2. computer architecture

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        • (2024)Brain-inspired computing systems: a systematic literature reviewThe European Physical Journal B10.1140/epjb/s10051-024-00703-697:6Online publication date: 6-Jun-2024

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