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Analog architectures for neural network acceleration based on non-volatile memory

Applied Physics Reviews

Xiao, T.P.; Bennett, Christopher H.; Feinberg, Benjamin F.; Agarwal, Sapan A.; Marinella, Matthew J.

Analog hardware accelerators, which perform computation within a dense memory array, have the potential to overcome the major bottlenecks faced by digital hardware for data-heavy workloads such as deep learning. Exploiting the intrinsic computational advantages of memory arrays, however, has proven to be challenging principally due to the overhead imposed by the peripheral circuitry and due to the non-ideal properties of memory devices that play the role of the synapse. We review the existing implementations of these accelerators for deep supervised learning, organizing our discussion around the different levels of the accelerator design hierarchy, with an emphasis on circuits and architecture. We explore and consolidate the various approaches that have been proposed to address the critical challenges faced by analog accelerators, for both neural network inference and training, and highlight the key design trade-offs underlying these techniques.

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TYPE Journal Article YEAR 2020
ScopusOSTIDOI

Extracting an Empirical Intermetallic Hydride Design Principle from Limited Data via Interpretable Machine Learning

Journal of Physical Chemistry Letters

Witman, Matthew; Ling, Sanliang; Grant, David M.; Walker, Gavin S.; Agarwal, Sapan A.; Stavila, Vitalie S.; Allendorf, Mark D.

An open question in the metal hydride community is whether there are simple, physics-based design rules that dictate the thermodynamic properties of these materials across the variety of structures and chemistry they can exhibit. While black box machine learning-based algorithms can predict these properties with some success, they do not directly provide the basis on which these predictions are made, therefore complicating the a priori design of novel materials exhibiting a desired property value. In this work we demonstrate how feature importance, as identified by a gradient boosting tree regressor, uncovers the strong dependence of the metal hydride equilibrium H2 pressure on a volume-based descriptor that can be computed from just the elemental composition of the intermetallic alloy. Elucidation of this simple structure-property relationship is valid across a range of compositions, metal substitutions, and structural classes exhibited by intermetallic hydrides. This permits rational targeting of novel intermetallics for high-pressure hydrogen storage (low-stability hydrides) by their descriptor values, and we predict a known intermetallic to form a low-stability hydride (as confirmed by density functional theory calculations) that has not yet been experimentally investigated.

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TYPE Journal Article YEAR 2020
ScopusOSTIDOI

Low-Voltage, CMOS-Free Synaptic Memory Based on Li XTiO2 Redox Transistors

ACS Applied Materials and Interfaces

Li, Yiyang; Fuller, Elliot J.; Asapu, Shiva; Agarwal, Sapan A.; Kurita, Tomochika; Yang, J.J.; Talin, A.A.

Neuromorphic computers based on analogue neural networks aim to substantially lower computing power by reducing the need to shuttle data between memory and logic units. Artificial synapses containing nonvolatile analogue conductance states enable direct computation using memory elements; however, most nonvolatile analogue memories require high write voltages and large current densities and are accompanied by nonlinear and unpredictable weight updates. Here, we develop an inorganic redox transistor based on electrochemical lithium-ion insertion into LiXTiO2 that displays linear weight updates at both low current densities and low write voltages. The write voltage, as low as 200 mV at room temperature, is achieved by minimizing the open-circuit voltage and using a low-voltage diffusive memristor selector. We further show that the LiXTiO2 redox transistor can achieve an extremely sharp transistor subthreshold slope of just 40 mV/decade when operating in an electrochemically driven phase transformation regime.

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TYPE Journal Article YEAR 2019
ScopusOSTIDOI
Results 26–50 of 126
Results 26–50 of 126