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Hardware-level Bayesian inference

Hardware-level Bayesian inference
Hardware-level Bayesian inference
Brain-inspired, inherently parallel computation has been proven to excel at tasks where the intrinsically serial Von Neumann architecture struggles. This has led to vast efforts aimed towards developing bio-inspired electronics, most notably in the guise of artificial neural networks (ANNs). However, ANNs are simply one possible substrate upon which computation can be carried out; their configuration determining what sort of computational function is being performed. In this work we show how Bayesian inference, a fundamental computational function, can be carried out using arrays of memristive devices, demonstrating computation directly using probability distributions as inputs and outputs. Our approach bypasses the need to map the Bayesian computation on an ANN (or any other) substrate since computation is carried out by simply providing the input distributions and letting Ohm’s law converge the voltages within the system to the correct answer. We show the fundamental circuit blocks used to enable this style of computation, examine how memristor non-idealities affect the quality of computation and exemplify a ‘Bayesian learning machine’ performing a simple task with no need for any digital arithmetic-logic operations.
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Manino, Edoardo
e5cec65c-c44b-45de-8255-7b1d8edfc04d
Messaris, Ioannis
312befd2-f699-4e85-9c52-6715aaa757d7
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Prodromakis, Themis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Manino, Edoardo
e5cec65c-c44b-45de-8255-7b1d8edfc04d
Messaris, Ioannis
312befd2-f699-4e85-9c52-6715aaa757d7
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Prodromakis, Themis
d58c9c10-9d25-4d22-b155-06c8437acfbf

Serb, Alexantrou, Manino, Edoardo, Messaris, Ioannis, Tran-Thanh, Long and Prodromakis, Themis (2017) Hardware-level Bayesian inference. In Neural Information Processing Systems. 7 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Brain-inspired, inherently parallel computation has been proven to excel at tasks where the intrinsically serial Von Neumann architecture struggles. This has led to vast efforts aimed towards developing bio-inspired electronics, most notably in the guise of artificial neural networks (ANNs). However, ANNs are simply one possible substrate upon which computation can be carried out; their configuration determining what sort of computational function is being performed. In this work we show how Bayesian inference, a fundamental computational function, can be carried out using arrays of memristive devices, demonstrating computation directly using probability distributions as inputs and outputs. Our approach bypasses the need to map the Bayesian computation on an ANN (or any other) substrate since computation is carried out by simply providing the input distributions and letting Ohm’s law converge the voltages within the system to the correct answer. We show the fundamental circuit blocks used to enable this style of computation, examine how memristor non-idealities affect the quality of computation and exemplify a ‘Bayesian learning machine’ performing a simple task with no need for any digital arithmetic-logic operations.

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Published date: December 2017

Identifiers

Local EPrints ID: 425616
URI: https://eprints.soton.ac.uk/id/eprint/425616
PURE UUID: 12be441a-a005-44fe-899b-a648399b987f
ORCID for Edoardo Manino: ORCID iD orcid.org/0000-0003-0028-5440
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316
ORCID for Themis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

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Date deposited: 26 Oct 2018 16:30
Last modified: 14 Mar 2019 01:38

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Contributors

Author: Alexantrou Serb
Author: Edoardo Manino ORCID iD
Author: Ioannis Messaris
Author: Long Tran-Thanh ORCID iD

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