96 citations · 260 across the 26 of their papers we have counts for
30 papers
NeuroAI and Beyond: Bridging Between Advances in Neuroscience and Artificial Intelligence
Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski +28
Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Sci…
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 Simulation of Drosophila Melanogaster Brain Connectome on Loihi 2
Felix Wang, Bradley H. Theilman, Fred Rothganger +3
We demonstrate the first-ever nontrivial, biologically realistic connectome simulated on neuromorphic computing hardware. Specifically, we implement the whole-brain connectome of t…
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…
AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators
Karan P. Patel, Andrew Maicke, Jared Arzate +6
Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consumi…
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…
Magnetic Tunnel Junction Random Number Generators Applied to Dynamically Tuned Probability Trees Driven by Spin Orbit Torque
Andrew Maicke, Jared Arzate, Samuel Liu +7
Perpendicular magnetic tunnel junction (pMTJ)-based true-random number generators (RNG) can consume orders of magnitude less energy per bit than CMOS pseudo-RNG. Here, we numerical…
Synaptic Sampling of Neural Networks
James B. Aimone, William Severa, J. Darby Smith
Probabilistic artificial neural networks offer intriguing prospects for enabling the uncertainty of artificial intelligence methods to be described explicitly in their function; ho…
Design Principles for Lifelong Learning AI Accelerators
Dhireesha Kudithipudi, Anurag Daram, Abdullah M. Zyarah +9
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The…
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…
Probabilistic Neural Circuits leveraging AI-Enhanced Codesign for Random Number Generation
Suma G. Cardwell, Catherine D. Schuman, J. Darby Smith +7
Stochasticity is ubiquitous in the world around us. However, our predominant computing paradigm is deterministic. Random number generation (RNG) can be a computationally inefficien…
Random Bitstream Generation using Voltage-Controlled Magnetic Anisotropy and Spin Orbit Torque Magnetic Tunnel Junctions
Samuel Liu, Jaesuk Kwon, Paul W. Bessler +6
Probabilistic computing using random number generators (RNGs) can leverage the inherent stochasticity of nanodevices for system-level benefits. The magnetic tunnel junction (MTJ) h…
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…
Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication
Ojas Parekh, Cynthia A. Phillips, Conrad D. James +1
Boolean circuits of McCulloch-Pitts threshold gates are a classic model of neural computation studied heavily in the late 20th century as a model of general computation. Recent adv…
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…
Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting
Aleksandra Faust, James B. Aimone, Conrad D. James +1
Robots and autonomous agents often complete goal-based tasks with limited resources, relying on imperfect models and sensor measurements. In particular, reinforcement learning (RL)…
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…
Tracking Cyber Adversaries with Adaptive Indicators of Compromise
Justin E. Doak, Joe B. Ingram, Sam A. Mulder +6
A forensics investigation after a breach often uncovers network and host indicators of compromise (IOCs) that can be deployed to sensors to allow early detection of the adversary i…