Timely reliable Bayesian decision-making enabled using memristors
Gorde:
| Argitaratua izan da: | arXiv.org (Dec 7, 2024), p. n/a |
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| Egile nagusia: | |
| Beste egile batzuk: | , , , , , , , , |
| Argitaratua: |
Cornell University Library, arXiv.org
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| Gaiak: | |
| Sarrera elektronikoa: | Citation/Abstract Full text outside of ProQuest |
| Etiketak: |
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MARC
| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3143054336 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2331-8422 | ||
| 035 | |a 3143054336 | ||
| 045 | 0 | |b d20241207 | |
| 100 | 1 | |a Song, Lekai | |
| 245 | 1 | |a Timely reliable Bayesian decision-making enabled using memristors | |
| 260 | |b Cornell University Library, arXiv.org |c Dec 7, 2024 | ||
| 513 | |a Working Paper | ||
| 520 | 3 | |a Brains perform timely reliable decision-making by Bayes theorem. Bayes theorem quantifies events as probabilities and, through probability rules, renders the decisions. Learning from this, applying Bayes theorem in practical problems can visualize the potential risks and decision confidence, thereby enabling efficient user-scene interactions. However, given the probabilistic nature, implementing Bayes theorem with the conventional deterministic computing can inevitably induce excessive computational cost and decision latency. Herein, we propose a probabilistic computing approach using memristors to implement Bayes theorem. We integrate volatile memristors with Boolean logics and, by exploiting the volatile stochastic switching of the memristors, realize Boolean operations with statistical probabilities and correlations, key for enabling Bayes theorem. To practically demonstrate the effectiveness of our memristor-enabled Bayes theorem approach in user-scene interactions, we design lightweight Bayesian inference and fusion operators using our probabilistic logics and apply the operators in road scene parsing for self-driving, including route planning and obstacle detection. The results show that our operators can achieve reliable decisions at a rate over 2,500 frames per second, outperforming human decision-making and the existing driving assistance systems. | |
| 653 | |a Advanced driver assistance systems | ||
| 653 | |a Bayesian analysis | ||
| 653 | |a Probabilistic inference | ||
| 653 | |a Boolean | ||
| 653 | |a Route planning | ||
| 653 | |a Frames per second | ||
| 653 | |a Decision making | ||
| 653 | |a Computing costs | ||
| 653 | |a Probability theory | ||
| 653 | |a Operators | ||
| 653 | |a Bayes Theorem | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Statistical inference | ||
| 653 | |a Memristors | ||
| 653 | |a Obstacle avoidance | ||
| 700 | 1 | |a Liu, Pengyu | |
| 700 | 1 | |a Liu, Yang | |
| 700 | 1 | |a Pei, Jingfang | |
| 700 | 1 | |a Cui, Wenyu | |
| 700 | 1 | |a Liu, Songwei | |
| 700 | 1 | |a Wen, Yingyi | |
| 700 | 1 | |a Teng, Ma | |
| 700 | 1 | |a Kong-Pang Pun | |
| 700 | 1 | |a Hu, Guohua | |
| 773 | 0 | |t arXiv.org |g (Dec 7, 2024), p. n/a | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3143054336/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch |
| 856 | 4 | 0 | |3 Full text outside of ProQuest |u http://arxiv.org/abs/2412.06838 |