Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses

In this paper, we present an alternative approach to neuromorphic systems based on multilevel resistive memory synapses and deterministic learning rules. We demonstrate an original methodology to use conductive-bridge RAM (CBRAM) devices as, easy to program and low-power, binary synapses with stocha...

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Vydané v:IEEE transactions on electron devices Ročník 60; číslo 7; s. 2402 - 2409
Hlavní autori: Suri, M., Querlioz, D., Bichler, O., Palma, G., Vianello, E., Vuillaume, D., Gamrat, C., DeSalvo, B.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York, NY IEEE 01.07.2013
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Applied sciences
Auditory learning
CBRAM synapse
Design. Technologies. Operation analysis. Testing
Electronics
Engineering Sciences
Exact sciences and technology
Immune system
Integrated circuits
Integrated circuits by function (including memories and processors)
Mathematical model
Neurons
Probabilistic logic
Programming
Semiconductor electronics. Microelectronics. Optoelectronics. Solid state devices
spike-timing dependent plasticity (STDP)
stochastic neuromorphic system
Switches
visual pattern extraction
Visualization
Performance evaluation
spike-timing dependent plasticity (STDP)
Random access memory
High precision
Emulator
Auditory learning
Shape detection
Stochastic system
visual pattern extraction
Implementation
Learning
Biomimetics
stochastic neuromorphic system
Non volatile memory
Energy dissipation
Plasticity
Robustness
Silicon
Deterministic approach
Multilevel system
Pattern extraction
Probabilistic approach
Circuit architecture
Synapse
CBRAM synapse
Integrated circuit
Low-power electronics
Visual Pattern Extraction
Index Terms—Stochastic Neuromorphic System
Auditory Learning
STDP
CBRAM Synapse
ISSN:0018-9383, 1557-9646
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Popis
Shrnutí:In this paper, we present an alternative approach to neuromorphic systems based on multilevel resistive memory synapses and deterministic learning rules. We demonstrate an original methodology to use conductive-bridge RAM (CBRAM) devices as, easy to program and low-power, binary synapses with stochastic learning rules. New circuit architecture, programming strategy, and probabilistic spike-timing dependent plasticity (STDP) learning rule for two different CBRAM configurations with-selector (1T-1R) and without-selector (1R) are proposed. We show two methods (intrinsic and extrinsic) for implementing probabilistic STDP rules. Fully unsupervised learning with binary synapses is illustrated through two example applications: 1) real-time auditory pattern extraction (inspired from a 64-channel silicon cochlea emulator); and 2) visual pattern extraction (inspired from the processing inside visual cortex). High accuracy (audio pattern sensitivity > 2, video detection rate > 95%) and low synaptic-power dissipation (audio 0.55 μW, video 74.2 μW) are shown. The robustness and impact of synaptic parameter variability on system performance are also analyzed.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9383
1557-9646
DOI:10.1109/TED.2013.2263000