If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Despite recent developments, deploying deep neural networks on resource constrained general purpose hardware remains a significant challenge. There has been much work in developing methods for reshaping neural networks, usually with a focus on minimising total parameter count. These methods are typically developed in a hardware-agnostic manner and do not exploit hardware behaviour. In this paper we propose a new approach, Hardware Aware Knowledge Distillation (HAKD) which uses empirical observations of hardware behaviour to design efficient student networks which are then trained with knowledge distillation. This allows the trade-off between accuracy and performance to be managed explicitly. We have applied this approach across three platforms and evaluated it on two networks, MobileNet and DenseNet, on CIFAR-10. We show that HAKD outperforms Deep Compression and Fisher pruning in terms of size, accuracy and performance.
Abstract--Convolutional Neural Networks (CNNs) are extremely computationally demanding, presenting a large barrier to their deployment on resource-constrained devices. Since such systems are where some of their most useful applications lie (e.g. In this paper we unify the two viewpoints in a Deep Learning Inference Stack and take an across-stack approach by implementing and evaluating the most common neural network compression techniques (weight pruning, channel pruning, and quantisation) and optimising their parallel execution with a range of programming approaches (OpenMP, OpenCL) and hardware architectures (CPU, GPU). We provide comprehensive Pareto curves to instruct tradeoffs under constraints of accuracy, execution time, and memory space. Recent years have yielded rapid advances in the field of deep learning, largely due to the unparalleled effectiveness of Convolutional Neural Networks (CNNs) on a variety of difficult problems . These networks are designed to run on servers with negligible resource constraints, utilising powerful GPUs. As such, creative approaches are required to deploy them on hardware with limited resources in order to enable many useful applications (e.g. However, currently these optimisation approaches come with limited benchmarks and few comparisons. We outline a first step towards a more comprehensive understanding of the performance available under different constraints of inference accuracy, execution time, and memory space. Since  used CNNs to outperform more traditional statistical techniques on the ImageNet dataset  they have become a standard tool for image processing. With a growing ecosystem dedicated to training deep neural networks, the number of parameters that state-of-the-art networks demand has vastly increased; in 2012 the state-of-the-art, AlexNet, had 61M parameters spread over eight layers whereas the most recent ImageNet winner uses an ensemble of SENets , the largest of which has 115M parameters across 154 layers.