Abstract
Hardware implementation of neuromorphic computing is attractive as a computing paradigm beyond the conventional digital computing. In this work, we show that the SET (off-to-on) transition of metal oxide resistive switching memory becomes probabilistic under a weak programming condition. The switching variability of the binary synaptic device implements a stochastic learning rule. Such stochastic SET transition was statistically measured and modeled for a simulation of a winner-take-all network for competitive learning. The simulation illustrates that with such stochastic learning, the orientation classification function of input patterns can be effectively realized. The system performance metrics were compared between the conventional approach using the analog synapse and the approach in this work that employs the binary synapse utilizing the stochastic learning. The feasibility of using binary synapse in the neurormorphic computing may relax the constraints to engineer continuous multilevel intermediate states and widens the material choice for the synaptic device design.
Highlights
In the memory hierarchy of today’s von Neumann digital system, the increasing gap between the caches and the non-volatile storage devices in terms of write/read speed has become the performance bottleneck of the whole system
Oxide based resistive switching memory is attractive for the large-scale demonstration of a neuromorphic system due to a relatively lower energy consumption, the compatibility with CMOS technology and the potential for 3D integration (Wong et al, 2012; Yu et al, 2013)
A hybrid neuromorphic system with CMOS neurons and oxide resistive switching synapses integrated on top of CMOS neurons at the metal interconnect layers can be envisioned
Summary
In the memory hierarchy of today’s von Neumann digital system, the increasing gap between the caches and the non-volatile storage devices in terms of write/read speed has become the performance bottleneck of the whole system. We demonstrate that the SET transition of the oxide synaptic device becomes probabilistic under a weak programming condition (applying a smaller voltage than the nominal switching voltage), we propose utilizing such switching variability to realize the stochastic learning rule in the binary synapse. For the analog synapse, increasing the RESET amplitude means that the RESET transition becomes less gradual and fewer intermediate states are available (Yu et al, 2012) As a result, both the selectivity and the orientation storage capacity decreases with increasing RESET pulse amplitude (see Figure 5F as an example). At the optimized programming condition for the binary synapse and the analog synapse, respectively, the same full network storage capacity of 100% is
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
