PULP-NN: accelerating quantized neural networks on parallel ultra-low-power RISC-V processors

Parallel Implementation of Neural Associative Memories on Risc Processors

Accelerating Deep Neural Networks on Low Power Heterogeneous Architectures

Deep learning applications are able to recognise images and speech with great accuracy, and their use is now everywhere in our daily lives. However, developing deep learning architectures such as deep neural networks in embedded systems is a challenging task because of the demanding computational resources and power consumption. Hence, sophisticated algorithms and methods that exploit the hardw...

Guest Editors’ Introduction Ultra-Low-Power Processors

Moore's Law and Ultra-Low-Power Processors

Welcome to the November/December 2017 special issue on ultra-low-power processors. The Internet of Things (IoT) revolution is happening at a rapid pace. Most projections forecast that the number of connected IoT devices will grow exponentially, easily reaching over 100 billion within the next decade. This revolution leads to a vigorous demand for ultra-low-power (ULP) edge computing devices and...

Adiabatic superconducting cells for ultra-low-power artificial neural networks

We propose the concept of using superconducting quantum interferometers for the implementation of neural network algorithms with extremely low power dissipation. These adiabatic elements are Josephson cells with sigmoid- and Gaussian-like activation functions. We optimize their parameters for application in three-layer perceptron and radial basis function networks.