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An adiabatic regenerative capacitive artificial neuron

An adiabatic regenerative capacitive artificial neuron
An adiabatic regenerative capacitive artificial neuron
In recent years, RRAM technology has been actively developed as a means of reducing power dissipation and area in a host of circuits, most notably artificial neuron synapses. However, further reduction in energy consumption may be possible by transitioning to capacitive synapses and combining them with adiabatic technique. In this work, we present and analyse the function and power dissipation of an artificial neuron with capacitive synapses where the synaptic tree is fed by a regenerative clock. Whilst the weights are fixed in this case, developments into memcapacitor technology offer the promise of tuneability in the future. In our example, a 4-synapse design was used as a proof-of-concept baseline at various frequencies. Our simulation at 1MHz indicates a ≈ 91% reduction of energy when using Regenerative Capacitive Synapses vs. standard, non-regenerative ones, which translates into a ≈ 35% drop in overall artificial neuron energy dissipation. The higher the ratio of synapses/soma, the higher the power savings, which is important for building larger and more complex neurons in silico.
Adiabatic technique, Artificial neuron, Capacitive synapses, Charge recovery, RRAM
0271-4310
1-5
IEEE
Maheshwari, Sachin
f09ac1de-0e3d-410d-a7e2-f4d54a1459b9
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Papavassiliou, Christos
86fe7042-20a3-47a9-9430-2bdb6c260303
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf
Maheshwari, Sachin
f09ac1de-0e3d-410d-a7e2-f4d54a1459b9
Serb, Alexantrou
30f5ec26-f51d-42b3-85fd-0325a27a792c
Papavassiliou, Christos
86fe7042-20a3-47a9-9430-2bdb6c260303
Prodromakis, Themistoklis
d58c9c10-9d25-4d22-b155-06c8437acfbf

Maheshwari, Sachin, Serb, Alexantrou, Papavassiliou, Christos and Prodromakis, Themistoklis (2021) An adiabatic regenerative capacitive artificial neuron. In 2021 IEEE International Symposium on Circuits and Systems, ISCAS 2021 - Proceedings. vol. 2021-May, IEEE. pp. 1-5 . (doi:10.1109/ISCAS51556.2021.9401142).

Record type: Conference or Workshop Item (Paper)

Abstract

In recent years, RRAM technology has been actively developed as a means of reducing power dissipation and area in a host of circuits, most notably artificial neuron synapses. However, further reduction in energy consumption may be possible by transitioning to capacitive synapses and combining them with adiabatic technique. In this work, we present and analyse the function and power dissipation of an artificial neuron with capacitive synapses where the synaptic tree is fed by a regenerative clock. Whilst the weights are fixed in this case, developments into memcapacitor technology offer the promise of tuneability in the future. In our example, a 4-synapse design was used as a proof-of-concept baseline at various frequencies. Our simulation at 1MHz indicates a ≈ 91% reduction of energy when using Regenerative Capacitive Synapses vs. standard, non-regenerative ones, which translates into a ≈ 35% drop in overall artificial neuron energy dissipation. The higher the ratio of synapses/soma, the higher the power savings, which is important for building larger and more complex neurons in silico.

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More information

Published date: 22 May 2021
Additional Information: Publisher Copyright: © 2021 IEEE Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Venue - Dates: 2021 IEEE 64th International Midwest Symposium on Circuits and Systems, 2021-08-09 - 2021-08-11
Keywords: Adiabatic technique, Artificial neuron, Capacitive synapses, Charge recovery, RRAM

Identifiers

Local EPrints ID: 453454
URI: http://eprints.soton.ac.uk/id/eprint/453454
ISSN: 0271-4310
PURE UUID: f33c8bc8-6b03-4588-97ea-1edfa3ec1d2b
ORCID for Themistoklis Prodromakis: ORCID iD orcid.org/0000-0002-6267-6909

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Date deposited: 17 Jan 2022 17:51
Last modified: 28 Apr 2022 02:09

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Contributors

Author: Sachin Maheshwari
Author: Alexantrou Serb
Author: Christos Papavassiliou
Author: Themistoklis Prodromakis ORCID iD

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