ArticleFrontiers in computational neuroscience2020
Probabilistic Circuits for Autonomous Learning: A Simulation Study.
Article in Frontiers in computational neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The 7E Teaching Model in Emergency Obstetrics and Gynecology Training: Enhancing Clinical Competency in Residency Education.Emergency medicine international · 2026Article
- Hardware Design for Autonomous Bayesian Networks.Frontiers in computational neuroscience · 2021Article
- Probabilistic Circuits for Autonomous Learning: A Simulation Study.Frontiers in computational neuroscience · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
Modern machine learning is based on powerful algorithms running on digital computing platforms and there is great interest in accelerating the learning process and making it more energy efficient. In this paper we present a fully autonomous probabilistic circuit for fast and efficient learning that makes no use of digital computing. Specifically we use SPICE simulations to demonstrate a clockless autonomous circuit where the required synaptic weights are read out in the form of analog voltages. This allows us to demonstrate a circuit that can be built with existing technology to emulate the Boltzmann machine learning algorithm based on gradient optimization of the maximum likelihood function. Such autonomous circuits could be particularly of interest as standalone learning devices in the context of mobile and edge computing.
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Registered trials
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