PCM-Trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials

Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing. To endow these systems with additional flexibility a...

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Bibliographic Details
Published in:IEEE International Symposium on Circuits and Systems proceedings pp. 1 - 5
Main Authors: Demirag, Yigit, Moro, Filippo, Dalgaty, Thomas, Navarro, Gabriele, Frenkel, Charlotte, Indiveri, Giacomo, Vianello, Elisa, Payvand, Melika
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2021
Subjects:
Hardware
Learning systems
Low-power electronics
Neural networks
Neuromorphics
System-on-chip
Task analysis
ISBN:9781728192017, 1728192013
ISSN:2158-1525
Online Access:Get full text
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Summary:Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing. To endow these systems with additional flexibility and the ability to learn to solve specific tasks, it is important to develop appropriate on-chip learning mechanisms. Recently, a new class of three-factor spike-based learning rules have been proposed that can solve the temporal credit assignment problem and approximate the error back-propagation algorithm on complex tasks. However, the efficient implementation of these rules on hybrid CMOS/memristive architectures is still an open challenge. Here we present a new neuromorphic building block, called PCM-trace, which exploits the drift behavior of phase- change materials to implement long lasting eligibility traces, a critical ingredient of three-factor learning rules. We demonstrate how the proposed approach improves the area efficiency by > 10× compared to existing solutions and demonstrates a technologically plausible learning algorithm supported by experimental data from device measurements.
ISBN:9781728192017
1728192013
ISSN:2158-1525
DOI:10.1109/ISCAS51556.2021.9401446