Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract 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,  demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight 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 beten 1,200 and 2,600 frames/s and using beten 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic por of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.
['Steven K. Esser', 'Paul A. Merolla', 'John V. Arthur', 'Andrew S. Cassidy', 'Rathinakumar Appuswamy', 'Alexander Andreopoulos', 'David J. Berg', 'Jeffrey L. McKinstry', 'Timothy Melano', 'Davis R', 'Carmelo di Nolfo', 'Pallab Datta', 'Arnon Amir', 'Brian Taba', 'Myron Flickner', 'Dharmendra S. Modha']
I am writing a research paper on a idea Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
[{"from": "human", "value": "I am writing a research paper on a idea Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Abstract 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,  demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware\u2019s underlying energy-efficiency and high throughput, running on the aforementioned datasets at beten 1,200 and 2,600 frames/s and using beten 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic por of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer."}]