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Found 9 papers, 1 papers with code
Neuromorphic Co-Design as a Game
In particular, we consider the interplay between algorithm and architecture advances in the field of neuromorphic computing.
NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems
The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.
Spiking Neural Streaming Binary Arithmetic
Boolean functions and binary arithmetic operations are central to standard computing paradigms.
RAPDARTS: Resource-Aware Progressive Differentiable Architecture Search
Gradient-based approaches impose more structure on the search, compared to alternative NAS methods, enabling faster search phase optimization.
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.
Distillation Strategies for Proximal Policy Optimization
DQN distillation extended the original distillation idea to transfer information stored in a high performance, high capacity teacher Q-function trained via the Deep Q-Learning (DQN) algorithm.
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.
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.
