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Yuan, X, Chai, Z, Zhou, X, Luo, Y, He, Y, Jian, J, Yue, X, Zhang, JF, Zhang, W and Min, T Hardware Estimation for the Eigenvectors of Stochastic Matrices using Magnetic Tunnel Junctions. IEEE Electron Device Letters. ISSN 0741-3106 (Accepted)
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EDL-2024-07-1340_Proof_hi.pdf - Accepted Version Download (2MB) | Preview |
Abstract
Matrices are the foundation of science and engineering. For artificial intelligence (AI) and Internet of Things (IoT) tasks, developing a hardware efficient way to find the eigenvector of stochastic matrix (SM) is urgently in need. In this paper, inspired by the divide-and-conquer strategy, we proposed a new hardware architecture, which uses magnetic tunnel junctions (MTJs) to estimate the eigenvector of an n×n SM where n is the power of 2. This approach reduces the required device amount to log 2 n by converting the larger SM into 2-state sub-SMs which are further represented by stochastic signals generated by MTJs. The validity of this method has been demonstrated and statistically evaluated. This method provides a novel hardware solution to solve mathematic problems using emerging hardware technologies.
| Item Type: | Article |
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| Additional Information: | © 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Uncontrolled Keywords: | 0906 Electrical and Electronic Engineering; Applied Physics |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Institute of Electrical and Electronics Engineers |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 06 Jan 2025 13:25 |
| Last Modified: | 06 Jan 2025 14:10 |
| DOI or ID number: | 10.1109/led.2024.3522890 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/25179 |
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