Cyber-attacks are becoming more sophisticated and thereby presenting increasing
challenges in accurately detecting intrusions. Failure to prevent the intrusions could degrade …

Approximate computing trades off computation quality with effort expended, and as rising
performance demands confront plateauing resource budgets, approximate computing has …

A number of recent efforts have attempted to design accelerators for popular machine
learning algorithms, such as those involving convolutional and deep neural networks (CNNs …

Neural networks (NNs) have been demonstrated to be useful in a broad range of
applications such as image recognition, automatic translation and advertisement …

In recent years, neural network accelerators have been shown to achieve both high energy
efficiency and high performance for a broad application scope within the important category …

In this paper, the performance of three deep learning methods for predicting short-term
evolution and for reproducing the long-term statistics of a multi-scale spatio-temporal Lorenz …

Machine-Learning tasks are becoming pervasive in a broad range of domains, and in a
broad range of systems (from embedded systems to data centers). At the same time, a small …

Continuous mobile vision is limited by the inability to efficiently capture image frames and
process vision features. This is largely due to the energy burden of analog readout circuitry …

Artificial Neural networks (ANNs) are one of the most well-established machine learning
techniques and have a wide range of applications, such as Recognition, Mining and …

Federated learning allows edge devices to collaboratively train a global model by
synchronizing their local updates without sharing private data. Yet, with limited network …