96 citations · 260 across the 26 of their papers we have counts for
16 papers · 1 filter
Intrinsic Numerical Robustness and Fault Tolerance in a Neuromorphic Algorithm for Scientific Computing
Bradley H. Theilman, James B. Aimone
The potential for neuromorphic computing to provide intrinsic fault tolerance has long been speculated, but the brain's robustness in neuromorphic applications has yet to be demons…
Neuromorphic Computing: A Theoretical Framework for Time, Space, and Energy Scaling
James B Aimone
Neuromorphic computing (NMC) is increasingly viewed as a low-power alternative to conventional von Neumann architectures such as central processing units (CPUs) and graphics proces…
Solving Sparse Finite Element Problems on Neuromorphic Hardware
Bradley H. Theilman, James B. Aimone
We demonstrate that scalable neuromorphic hardware can implement the finite element method, which is a critical numerical method for engineering and scientific discovery. Our appro…
Neuromorphic Co-Design as a Game
Craig M. Vineyard, William M. Severa, James B. Aimone
Co-design is a prominent topic presently in computing, speaking to the mutual benefit of coordinating design choices of several layers in the technology stack. For example, this ma…
Decomposing spiking neural networks with Graphical Neural Activity Threads
Bradley H. Theilman, Felix Wang, Fred Rothganger +1
A satisfactory understanding of information processing in spiking neural networks requires appropriate computational abstractions of neural activity. Traditionally, the neural popu…
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…
Sequence Learning and Consolidation on Loihi using On-chip Plasticity
Jack Lindsey, James B Aimone
In this work we develop a model of predictive learning on neuromorphic hardware. Our model uses the on-chip plasticity capabilities of the Loihi chip to remember observed sequences…
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…
Neuromorphic scaling advantages for energy-efficient random walk computation
J. Darby Smith, Aaron J. Hill, Leah E. Reeder +5
Computing stands to be radically improved by neuromorphic computing (NMC) approaches inspired by the brain's incredible efficiency and capabilities. Most NMC research, which aims t…
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…
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…
Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication
William Severa, Craig M. Vineyard, Ryan Dellana +2
This paper presents a new technique for training networks for low-precision communication. Targeting minimal communication between nodes not only enables the use of emerging spikin…
Spiking Neural Algorithms for Markov Process Random Walk
William Severa, Rich Lehoucq, Ojas Parekh +1
The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications. We consider here two neural algorithms that can be use…
Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation
William M. Severa, Jerilyn A. Timlin, Suraj Kholwadwala +2
The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements. Motivated by the potential for an in-the-field cell sorting de…
Exponential scaling of neural algorithms - a future beyond Moore's Law?
James B. Aimone
Although the brain has long been considered a potential inspiration for future computing, Moore's Law - the scaling property that has seen revolutions in technologies ranging from…
Neurogenesis Deep Learning
Timothy J. Draelos, Nadine E. Miner, Christopher C. Lamb +6
Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks. These methods have arguably had…