Memristor design rules for dynamic learning and edge processing applications
The ability to dynamically learn and adapt to changes in the environment is one of the hallmarks of biological systems. In this work, we identify the subset of the design space of memristive materials that is optimal for dynamic learning applications. Using an architecture inspired on the learning center of the insect brain, we implement a model system consisting of a discrete implementation of spiking neurons where dynamic learning takes place on a set of plastic synapses formed by memristor pairs in a crossbar array. Using two separate benchmarks, one comprising the dynamic learning of the Modified National Institute of Standards and Technology dataset and another one targeting one shot learning, we have identified the key properties that memristive materials should have to be optimal dynamic learners. The results obtained show that a fine degree of control of the memristor internal state is key to achieve high classification accuracy during dynamic learning but that within this optimal region learning is extremely robust both to device variability and to errors in the writing of the internal state, in all cases allowing for 2 sigma variations greater than 40% without significant loss of accuracy, hence overcoming two of the perceived limitations of memristors. By additionally requiring that learning takes place concurrently to information processing, we are able to derive a set constraints to the memristor dynamics.
- Research Organization:
- Argonne National Laboratory (ANL), Argonne, IL (United States)
- Sponsoring Organization:
- U.S. Department of Defense (DOD), Defense Advanced Research Projects Agency (DARPA); USDOE Office of Science (SC)
- Grant/Contract Number:
- AC02-06CH11357
- OSTI ID:
- 1578165
- Alternate ID(s):
- OSTI ID: 1560347
- Journal Information:
- APL Materials, Vol. 7, Issue 9; ISSN 2166-532X
- Publisher:
- American Institute of Physics (AIP)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Emerging materials in neuromorphic computing: Guest editorial
|
journal | January 2020 |
| Coarse scale representation of spiking neural networks: backpropagation through spikes and application to neuromorphic hardware | preprint | January 2020 |
| Neuromodulated Neural Architectures with Local Error Signals for Memory-Constrained Online Continual Learning | preprint | January 2020 |
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