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Found 5 papers, 3 papers with code
Language Modeling using LMUs: 10x Better Data Efficiency or Improved Scaling Compared to Transformers
Over three orders of magnitude, we show that our new architecture attains the same accuracy as transformers with 10x fewer tokens.
A Spiking Neural Network for Image Segmentation
We explore the advantages of regularizing firing rates of Loihi neurons for converting ANN to SNN with minimum accuracy loss and optimized energy consumption.
Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware
Using Intel's Loihi neuromorphic research chip and ABR's Nengo Deep Learning toolkit, we analyze the inference speed, dynamic power consumption, and energy cost per inference of a two-layer neural network keyword spotter trained to recognize a single phrase.
Training Spiking Deep Networks for Neuromorphic Hardware
We describe a method to train spiking deep networks that can be run using leaky integrate-and-fire (LIF) neurons, achieving state-of-the-art results for spiking LIF networks on five datasets, including the large ImageNet ILSVRC-2012 benchmark.
Spiking Deep Networks with LIF Neurons
We train spiking deep networks using leaky integrate-and-fire (LIF) neurons, and achieve state-of-the-art results for spiking networks on the CIFAR-10 and MNIST datasets.
