Hardware Architectures for Deep Learning provides an overview of this new field,from principles to applications,for researchers,postgraduate students and engineers who work on learning-based services and hardware platforms. Hardware Architectures for Deep Learning 9781785617683.pdf[/erphpdown]...
Deep learning architectures (DLA) have shown impressive performance in computer vision, natural language processing and so on. Many DLA make use of cloud computing to achieve classification due to the high computation and memory requirements. Privacy and latency concerns resulting from cloud computing ...
(ML) in a variety of applications. This article aims at providing a comprehensive survey on summarizing recent trends and advances in hardware accelerator design for machine learning based on various hardware platforms like ASIC, FPGA and GPU. In this article, we look at different architectures ...
it has been a race between FPGA and GPU vendors to offer a HW platform that runs computationally intensive machine learning algorithms fast and efficiently. As Deep Learning has driven most of the advanced machine learning applications, it is regarded as the main comparison ...
In this paper, we present a novel technique to search for hardware architectures of accelerators optimized for end-to-end training of deep neural networks (DNNs). Our approach addresses both single-device and distributed pipeline and tensor model parallel scenarios,...
To illustrate the operation of the design platform, two well-known deep CNNs are used, which are YOLOv3 and faster RCNN. This technology can be used to explore and to optimize the hardware architectures of the CNNs so that the cost can be minimized....
SheffieldUnited KingdomArtificial IntelligenceComputer ArchitecturesComputer VisionElectronic EngineeringMachine Learning About the Project Artificial Intelligence (AI) is becoming the default choice for the many of the applications in the industries such as image processing and pattern recognition. As a result...
As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are
Designing efficient artificial neural network circuit architectures for optimal information routing remains a challenge. Here, the authors propose “Mosaic", the first demonstration of on-chip in-memory spike routing using memristors, optimized for small-world graphs prevalent in mammalian brains, offering...
We are pleased to share that eight papers from Microsoft researchers and their collaborators have been accepted to the conference, spanning a broad spectrum of topics. In the field of AI and deep learning, subjects include power and frequency...