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ilkarman/DeepLearningFrameworks: Demo of running NNs across different frameworks
GitHub is where people build software. More than 27 million people use GitHub to discover, fork, and contribute to over 80 million projects.
deep-learning  frameworks  comparison 
yesterday by hschilling
[1603.08270] Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that i) approach state-of-the-art classification accuracy across 8 standard datasets, encompassing vision and speech, ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1200 and 2600 frames per second and using between 25 and 275 mW (effectively > 6000 frames / sec / W) and iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. For the first time, the algorithmic power of deep learning can be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.
deep-learning  neural-networks  review  optimization  algorithms  hardware 
yesterday by Vaguery

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