1,389 research outputs found
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Spiking neural networks (SNN) are artificial computational models that have
been inspired by the brain's ability to naturally encode and process
information in the time domain. The added temporal dimension is believed to
render them more computationally efficient than the conventional artificial
neural networks, though their full computational capabilities are yet to be
explored. Recently, computational memory architectures based on non-volatile
memory crossbar arrays have shown great promise to implement parallel
computations in artificial and spiking neural networks. In this work, we
experimentally demonstrate for the first time, the feasibility to realize
high-performance event-driven in-situ supervised learning systems using
nanoscale and stochastic phase-change synapses. Our SNN is trained to recognize
audio signals of alphabets encoded using spikes in the time domain and to
generate spike trains at precise time instances to represent the pixel
intensities of their corresponding images. Moreover, with a statistical model
capturing the experimental behavior of the devices, we investigate
architectural and systems-level solutions for improving the training and
inference performance of our computational memory-based system. Combining the
computational potential of supervised SNNs with the parallel compute power of
computational memory, the work paves the way for next-generation of efficient
brain-inspired systems
Monatomic phase change memory
Phase change memory has been developed into a mature technology capable of
storing information in a fast and non-volatile way, with potential for
neuromorphic computing applications. However, its future impact in electronics
depends crucially on how the materials at the core of this technology adapt to
the requirements arising from continued scaling towards higher device
densities. A common strategy to finetune the properties of phase change memory
materials, reaching reasonable thermal stability in optical data storage,
relies on mixing precise amounts of different dopants, resulting often in
quaternary or even more complicated compounds. Here we show how the simplest
material imaginable, a single element (in this case, antimony), can become a
valid alternative when confined in extremely small volumes. This compositional
simplification eliminates problems related to unwanted deviations from the
optimized stoichiometry in the switching volume, which become increasingly
pressing when devices are aggressively miniaturized. Removing compositional
optimization issues may allow one to capitalize on nanosize effects in
information storage
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