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Found 4 papers, 1 papers with code
Hardware-aware training of models with synaptic delays for digital event-driven neuromorphic processors
In this work, we propose a framework to train and deploy, in digital neuromorphic hardware, highly performing spiking neural network models (SNNs) where apart from the synaptic weights, the per-synapse delays are also co-optimized.
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.
Open the box of digital neuromorphic processor: Towards effective algorithm-hardware co-design
In this work, we open the black box of the digital neuromorphic processor for algorithm designers by presenting the neuron processing instruction set and detailed energy consumption of the SENeCA neuromorphic architecture.
Training for temporal sparsity in deep neural networks, application in video processing
Spatial sparsification of activations is a popular topic in DNN literature and several methods have already been established to bias a DNN for it.
