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Solving Sparse Finite Element Problems on Neuromorphic Hardware
We demonstrate that scalable neuromorphic hardware can implement the finite element method, which is a critical numerical method for engineering and scientific discovery.
AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators
Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems.
Neuromorphic Co-Design as a Game
In particular, we consider the interplay between algorithm and architecture advances in the field of neuromorphic computing.
Synaptic Sampling of Neural Networks
Probabilistic artificial neural networks offer intriguing prospects for enabling the uncertainty of artificial intelligence methods to be described explicitly in their function; however, the development of techniques that quantify uncertainty by well-understood methods such as Monte Carlo sampling has been limited by the high costs of stochastic sampling on deterministic computing hardware.
Design Principles for Lifelong Learning AI Accelerators
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).
Decomposing spiking neural networks with Graphical Neural Activity Threads
A satisfactory understanding of information processing in spiking neural networks requires appropriate computational abstractions of neural activity.
Stochastic Neuromorphic Circuits for Solving MAXCUT
By designing circuits and algorithms that make use of randomness similarly to natural brains, we hypothesize that the intrinsic randomness in microelectronics devices could be turned into a valuable component of a neuromorphic architecture enabling more efficient computations.
Spiking Neural Streaming Binary Arithmetic
Boolean functions and binary arithmetic operations are central to standard computing paradigms.
Neuromorphic scaling advantages for energy-efficient random walk computation
Computing stands to be radically improved by neuromorphic computing (NMC) approaches inspired by the brain's incredible efficiency and capabilities.
Constant-Depth and Subcubic-Size Threshold Circuits for Matrix Multiplication
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.
Solving a steady-state PDE using spiking networks and neuromorphic hardware
The widely parallel, spiking neural networks of neuromorphic processors can enable computationally powerful formulations.
Composing Neural Algorithms with Fugu
Rather than necessitating a developer attain intricate knowledge of how to program and exploit spiking neural dynamics to utilize the potential benefits of neuromorphic computing, Fugu is designed to provide a higher level abstraction as a hardware-independent mechanism for linking a variety of scalable spiking neural algorithms from a variety of sources.
Whetstone: A Method for Training Deep Artificial Neural Networks for Binary Communication
We present a method for training deep spiking neural networks using an iterative modification of the backpropagation optimization algorithm.
Resilient Computing with Reinforcement Learning on a Dynamical System: Case Study in Sorting
Robots and autonomous agents often complete goal-based tasks with limited resources, relying on imperfect models and sensor measurements.
Spiking Neural Algorithms for Markov Process Random Walk
The random walk is a fundamental stochastic process that underlies many numerical tasks in scientific computing applications.
Context-modulation of hippocampal dynamics and deep convolutional networks
Complex architectures of biological neural circuits, such as parallel processing pathways, has been behaviorally implicated in many cognitive studies.
Dynamic Analysis of Executables to Detect and Characterize Malware
It is needed to ensure the integrity of systems that process sensitive information and control many aspects of everyday life.
Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation
The high dimensionality of hyperspectral imaging forces unique challenges in scope, size and processing requirements.
Exponential scaling of neural algorithms - a future beyond Moore's Law?
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 supercomputers to smart phones - has largely been driven by advances in materials science.
A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines
Information in neural networks is represented as weighted connections, or synapses, between neurons.
Neurogenesis Deep Learning
Neural machine learning methods, such as deep neural networks (DNN), have achieved remarkable success in a number of complex data processing tasks.
