Energy Efficient Approximate Arithmetic for Error Resilient Neuromorphic Computing

Energy Efficient and Error Resilient Neuromorphic Computing in Vlsi

Realization of the conventional Von Neumann architecture faces increasing challenges due to growing process variations, device reliability and power consumption. As an appealing architectural solution, brain-inspired neuromorphic computing has drawn a great deal of research interest due to its potential improved scalability and power efficiency, and better suitability in processing complex task...

Approximate computing for energy-efficient error-resilient multimedia systems

The rapid advancement in scaled silicon technology has resulted in the influx of numerous consumer devices with a plethora of applications. Multimedia applications which use image and video processing, pattern or facial recognition, data mining and synthesis have seen a significant increase in user base. These applications not only demand complex signal processing of digital data to achieve qua...

Backpropagation for Energy-Efficient Neuromorphic Computing

Solving real world problems with embedded neural networks requires both training algorithms that achieve high performance and compatible hardware that runs in real time while remaining energy efficient. For the former, deep learning using backpropagation has recently achieved a string of successes across many domains and datasets. For the latter, neuromorphic chips that run spiking neural netwo...

Design of Energy - Efficient Approximate Arithmetic Circuits

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 ...