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Found 12 papers, 0 papers with code
The Robustness of Spiking Neural Networks in Communication and its Application towards Network Efficiency in Federated Learning
Spiking Neural Networks (SNNs) have recently gained significant interest in on-chip learning in embedded devices and emerged as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs).
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.
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.
MIKE - Multi-task Implicit Knowledge Embeddings by Autoencoding through a Shared Input Space
In this work, we introduce a method of learning Multi-task Implicit Knowledge Embeddings (MIKE) from a set of source (or "teacher") networks by autoencoding through a shared input space.
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.
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.
Hyperparameter Optimization in Binary Communication Networks for Neuromorphic Deployment
In this work, we introduce a Bayesian approach for optimizing the hyperparameters of an algorithm for training binary communication networks that can be deployed to neuromorphic hardware.
Evaluating complexity and resilience trade-offs in emerging memory inference machines
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 potential disturbance.
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.
Making BREAD: Biomimetic strategies for Artificial Intelligence Now and in the Future
First, that scientific societies and governments coordinate Biomimetic Research for Energy-efficient, AI Designs (BREAD); a multinational initiative and a funding strategy for investments in the future integrated design of energetics into AI.
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.
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.
