Designing resource-efficient deep neural networks (DNNs) is a challenging task due to the
enormous diversity of applications as well as their time-consuming design, training …

In this paper, we present a novel multi-objective hardware-aware neural architecture search
(NAS) framework, namely HSCoNAS, to automate the design of deep neural networks …

The efficiency of deep neural network (DNN) solutions on real hardware devices are mainly
decided by the DNN architecture and the compiler-level scheduling strategy on the …

Hardware systems integrated with deep neural networks (DNNs) are deemed to pave the
way for future artificial intelligence (AI). However, manually designing efficient DNNs …

Deep neural networks (DNN) are now dominating in the most challenging applications of
machine learning. As DNNs can have complex architectures with millions of trainable …

Neural network search (NAS) projects a promising direction to automate the design process
of efficient and powerful neural network architectures. Nevertheless, the NAS techniques …

Embedded deep learning platforms have witnessed two simultaneous improvements. First,
the accuracy of convolutional neural networks (CNNs) has been significantly improved …

Neural architecture search (NAS) is an emerging paradigm to automate the design of
competitive deep neural networks (DNNs). In practice, DNNs are subject to strict latency …

The continued success of Deep Neural Networks (DNNs) in classification tasks has sparked
a trend of accelerating their execution with specialized hardware. While published designs …

In hardware-aware Differentiable Neural Architecture Search (DNAS), it is challenging to
compute gradients of hardware metrics to perform architecture search. Existing works rely on …