Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Automated machine learning has been widely explored to reduce human efforts in
designing neural architectures and looking for proper hyperparameters. In the domain of …

A key challenge in neural architecture search (NAS) is quickly inferring the predictive
performance of a broad spectrum of networks to discover statistically accurate and …

As emerging applications are rapidly moving to accelerators, a greatdeal of research has
been proposed to improve the performance of the accelerators. For the AI applications …

Recently, memory consumption of Deep Neural Network (DNN) rapidly increases, mainly
due to long lifetimes and large shapes of tensors. Graph scheduling has emerged as an …

Neural architecture search (NAS) has become an increasingly important tool within the deep
learning community in recent years, yielding many practical advancements in the design of …

The demands for higher performance and accuracy in neural networks (NNs) never end.
Existing tensor compilation and Neural Architecture Search (NAS) techniques orthogonally …

The desires for better prediction accuracy and higher execution performance in neural
networks never end. Neural architecture search (NAS) and tensor compilers are two popular …

Running machine learning (ML) on low-power IoT devices exposes unique security
concerns. Attackers can easily steal or manipulate sensitive user data or proprietary ML …

Deep Learning models have experienced exponential growth in complexity and resource
demands in recent years. Accelerating these models for efficient execution on resource …