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Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses.

ABSTRACT: We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

SUBMITTER: Ambrogio S

PROVIDER: S-EPMC4781832 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses.

Ambrogio Stefano S Ciocchini Nicola N Laudato Mario M Milo Valerio V Pirovano Agostino A Fantini Paolo P Ielmini Daniele D

Frontiers in neuroscience 20160308

We present a novel one-transistor/one-resistor (1T1R) synapse for neuromorphic networks, based on phase change memory (PCM) technology. The synapse is capable of spike-timing dependent plasticity (STDP), where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, ...[more]

PMID: 27013934

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Project description:Ion-conducting memristors comprised of the layered materials Ge2Se3/SnSe/Ag are promising candidates for neuromorphic computing applications. Here, the spike-timing dependent plasticity (STDP) application is demonstrated for the first time with a single memristor type operating as a synapse over a timescale of 10 orders of magnitude, from nanoseconds through seconds. This large dynamic range allows the memristors to be useful in applications that require slow biological times, as well as fast times such as needed in neuromorphic computing, thus allowing multiple functions in one design for one memristor type-a "one size fits all" approach. This work also investigated the effects of varying the spike pulse shapes on the STDP response of the memristors. These results showed that small changes in the pre- and postsynaptic pulse shape can have a significant impact on the STDP. These results may provide circuit designers with insights into how pulse shape affects the actual memristor STDP response and aid them in the design of neuromorphic circuits and systems that can take advantage of certain features in the memristor STDP response that are programmable via the pre- and postsynaptic pulse shapes. In addition, the energy requirement per memristor is approximated based on the pulse shape and timing responses. The energy requirement estimated per memristor operating on slower biological timescales (milliseconds to seconds) is larger (nanojoules range), as expected, than the faster (nanoseconds) operating times (~0.1 pJ in some cases). Lastly, the memristors responded in a similar manner under normal STDP conditions (pre- and post-spikes applied to opposite memristor terminals) as they did to the case where a waveform corresponding to the difference between pre- and post-spikes was applied to only one electrode, with the other electrode held at ground potential. By applying the difference signal to only one terminal, testing of the memristor in various applications can be achieved with a simplified test set-up, and thus be easier to accomplish in most laboratories.

Dataset Information

Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses.

Publications

Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses.

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