Proof-of-Useful-Work Blockchain for Trustworthy Biomedical Hyperdimensional Computing
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866909727437357056 |
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| author | Wen, Jinghao Ma, Dongning Zhang, Sizhe Sudler, Hasshi Jiao, Xun |
| author_facet | Wen, Jinghao Ma, Dongning Zhang, Sizhe Sudler, Hasshi Jiao, Xun |
| contents | Hyperdimensional Computing (HDC) is a promising bio-inspired learning paradigm for its advantage of balancing performance and efficiency and has been increasingly applied to the bio-medical domain. In bio-medical applications, trustworthiness such as replicability and verifiability of the trained learning models is crucial. In this work, we introduce HDCoin, the first proof-of-useful-work blockchain framework for HDC. With HDCoin, we transform the conventional energy-wasteful mining process into a competitive process for developing high accuracy, trustworthy and verifiable hyperdimensional models. We explore four diverse biomedical datasets, and conduct an extensive design-space exploration of key HDC hyperparameters of blockchain miners such as dimensionality, learning rate, and retraining iterations for model performance, adaptive mining difficulty and fairness on proof-of-useful-work. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2202_02964 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Proof-of-Useful-Work Blockchain for Trustworthy Biomedical Hyperdimensional Computing Wen, Jinghao Ma, Dongning Zhang, Sizhe Sudler, Hasshi Jiao, Xun Cryptography and Security Neural and Evolutionary Computing Hyperdimensional Computing (HDC) is a promising bio-inspired learning paradigm for its advantage of balancing performance and efficiency and has been increasingly applied to the bio-medical domain. In bio-medical applications, trustworthiness such as replicability and verifiability of the trained learning models is crucial. In this work, we introduce HDCoin, the first proof-of-useful-work blockchain framework for HDC. With HDCoin, we transform the conventional energy-wasteful mining process into a competitive process for developing high accuracy, trustworthy and verifiable hyperdimensional models. We explore four diverse biomedical datasets, and conduct an extensive design-space exploration of key HDC hyperparameters of blockchain miners such as dimensionality, learning rate, and retraining iterations for model performance, adaptive mining difficulty and fairness on proof-of-useful-work. |
| title | Proof-of-Useful-Work Blockchain for Trustworthy Biomedical Hyperdimensional Computing |
| topic | Cryptography and Security Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2202.02964 |