1 citations · 1 across the 8 of their papers we have counts for
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Stochastic Neuromorphic Circuits for Solving MAXCUT
Bradley H. Theilman, Yipu Wang, Ojas D. Parekh +3
Finding the maximum cut of a graph (MAXCUT) is a classic optimization problem that has motivated parallel algorithm development. While approximate algorithms to MAXCUT offer attrac…
Spiking Neural Streaming Binary Arithmetic
James B. Aimone, Aaron J. Hill, William M. Severa +1
Boolean functions and binary arithmetic operations are central to standard computing paradigms. Accordingly, many advances in computing have focused upon how to make these operatio…
Solving a steady-state PDE using spiking networks and neuromorphic hardware
J. Darby Smith, William Severa, Aaron J. Hill +5
The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations. While recent interest has focused on primarily machine lea…
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
Maryam Parsa, Catherine D. Schuman, Prasanna Date +8
Training neural networks for neuromorphic deployment is non-trivial. There have been a variety of approaches proposed to adapt back-propagation or back-propagation-like algorithms…
Evaluating complexity and resilience trade-offs in emerging memory inference machines
Christopher H. Bennett, Ryan Dellana, T. Patrick Xiao +6
Neuromorphic-style inference only works well if limited hardware resources are maximized properly, e.g. accuracy continues to scale with parameters and complexity in the face of po…
Composing Neural Algorithms with Fugu
James B Aimone, William Severa, Craig M Vineyard
Neuromorphic hardware architectures represent a growing family of potential post-Moore's Law Era platforms. Largely due to event-driving processing inspired by the human brain, the…