Timely reliable Bayesian decision-making enabled using memristors

I tiakina i:

Ngā taipitopito rārangi puna kōrero
I whakaputaina i:	arXiv.org (Dec 7, 2024), p. n/a
Kaituhi matua:	Song, Lekai
Ētahi atu kaituhi:	Liu, Pengyu, Liu, Yang, Pei, Jingfang, Cui, Wenyu, Liu, Songwei, Wen, Yingyi, Teng, Ma, Kong-Pang Pun, Hu, Guohua
I whakaputaina:	Cornell University Library, arXiv.org
Ngā marau:	Advanced driver assistance systems Bayesian analysis Probabilistic inference Boolean Route planning Frames per second Decision making Computing costs Probability theory Operators Bayes Theorem Statistical analysis Statistical inference Memristors Obstacle avoidance
Urunga tuihono:	Citation/Abstract Full text outside of ProQuest
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Whakaahuatanga
Whakarāpopotonga:	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.
ISSN:	2331-8422
Puna:	Engineering Database