Making deep convolutional neural networks more accurate typically comes at the cost of
increased computational and memory resources. In this paper, we reduce this cost by …

Modern deep Convolutional Neural Networks (CNNs) are computationally demanding, yet
real applications often require high throughput and low latency. To help tackle these …

In this paper, we propose a deep reinforcement learning (DRL) based framework to
efficiently perform runtime channel pruning on convolutional neural networks (CNNs). Our …

Deep convolutional neural networks (CNNs) are powerful tools for a wide range of vision
tasks, but the enormous amount of memory and compute resources required by CNNs …

Deep convolutional neural networks have been proved successful on a wide range of tasks,
yet they are still hindered by their large computation cost in many industrial scenarios. In this …

Hardware accelerators for inference with neural networks can take advantage of the
properties of data they process. Performance gains and reduced memory bandwidth during …

Increasing interest in deep learning and convolutional neural networks resulted in the last
years in multiple techniques aiming to improve their accuracy, training speed, and inference …

The continued increase in memory, runtime and energy consumption of deployed machine
learning models on one side, and the trend to miniaturize intelligent devices and sensors on …

In today's world, a vast amount of data is being generated by edge devices that can be used
as valuable training data to improve the performance of machine learning algorithms in …

The growing complexity of data processing systems has long led systems designers to
imagine systems (eg databases, schedulers) which can self-configure and adapt based on …