Neural Networks for Space-Based Event Detection
This poster describes progress in training a variety of neural network architectures on a typical detection task. The poster will be presented at the Sandia MLDL Workshop
Engineering topics
Publications and source records attributed to Bennett, Christopher.
This poster describes progress in training a variety of neural network architectures on a typical detection task. The poster will be presented at the Sandia MLDL Workshop
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Plenary lightning talk for DOE ASCR BRN workshop on analog computing
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RADECS 2024 submission
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An ionic redox transistor comprises a solid channel, a solid reservoir layer, and a solid electrolyte layer disposed between the channel and the reservoir layer. The channel exhibits a substantially linear current-voltage relationship in a first range of voltages, and a nonlinear current-voltage relationship in a second range of voltages that is greater than the first range of voltages. One or both of the substantially linear current-voltage relationship or the nonlinear current-voltage relationship of the channel is varied by changing the concentration of ions such as oxygen vacancies in the channel. Ion or vacancy transport between the channel and the reservoir layer across the electrolyte layer occurs in response to applying a voltage between the channel and the reservoir layer. Subject to the first range of voltages, the channel can function as a synapse device. Subject to the second range of voltages, the channel can function as a neuron device.
Analysis of electrical noise in vanadium oxide electrochemical random access memory devices and its potential for application in a Bayesian neural network
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SAND2024-08549O PyTorch Circuit-Aware Bit-Cell Modeling, a Python library, demonstrates circuit-aware training for analog machine learning accelerators. Built upon PyTorch, this software provides hardware-aware versions of common neural network layers to mitigate non-ideal behavior from novel hardware. The software provides accurate forward and backward pass estimates for hardware-mapped neural networks that are implemented in crossbars (arrays) that contain a non-linear transistor selector device. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
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Abstract not provided.
Abstract not provided.
Abstract not provided.