7 research outputs found
Scientific Image Restoration Anywhere
The use of deep learning models within scientific experimental facilities
frequently requires low-latency inference, so that, for example, quality
control operations can be performed while data are being collected. Edge
computing devices can be useful in this context, as their low cost and compact
form factor permit them to be co-located with the experimental apparatus. Can
such devices, with their limited resources, can perform neural network
feed-forward computations efficiently and effectively? We explore this question
by evaluating the performance and accuracy of a scientific image restoration
model, for which both model input and output are images, on edge computing
devices. Specifically, we evaluate deployments of TomoGAN, an image-denoising
model based on generative adversarial networks developed for low-dose x-ray
imaging, on the Google Edge TPU and NVIDIA Jetson. We adapt TomoGAN for edge
execution, evaluate model inference performance, and propose methods to address
the accuracy drop caused by model quantization. We show that these edge
computing devices can deliver accuracy comparable to that of a full-fledged CPU
or GPU model, at speeds that are more than adequate for use in the intended
deployments, denoising a 1024 x 1024 image in less than a second. Our
experiments also show that the Edge TPU models can provide 3x faster inference
response than a CPU-based model and 1.5x faster than an edge GPU-based model.
This combination of high speed and low cost permits image restoration anywhere.Comment: 6 pages, 8 figures, 1 tabl
Exploration of TPUs for AI Applications
Tensor Processing Units (TPUs) are specialized hardware accelerators for deep
learning developed by Google. This paper explores the performance of TPU with a
focus on AI and its implementation in edge computing. It first provides an
overview of TPUs, specifically their design in relation to neural networks,
their general architecture, compilation techniques and supporting frameworks.
Furthermore, we provide a comparative analysis of Cloud and Edge TPU
performance against other counterpart chip architectures. It is then discussed
how TPUs can be used to speed up AI workloads. The results show that TPUs can
provide significant performance improvements both in cloud and edge computing.
Additionally, we address the need for further research for the deployment of
more architectures in the Edge TPU, as well as the need for the development of
more robust comparisons in edge computing.Comment: Research done by the Robotics & AI Club at IE Universit
Neural Network Methods for Radiation Detectors and Imaging
Recent advances in image data processing through machine learning and
especially deep neural networks (DNNs) allow for new optimization and
performance-enhancement schemes for radiation detectors and imaging hardware
through data-endowed artificial intelligence. We give an overview of data
generation at photon sources, deep learning-based methods for image processing
tasks, and hardware solutions for deep learning acceleration. Most existing
deep learning approaches are trained offline, typically using large amounts of
computational resources. However, once trained, DNNs can achieve fast inference
speeds and can be deployed to edge devices. A new trend is edge computing with
less energy consumption (hundreds of watts or less) and real-time analysis
potential. While popularly used for edge computing, electronic-based hardware
accelerators ranging from general purpose processors such as central processing
units (CPUs) to application-specific integrated circuits (ASICs) are constantly
reaching performance limits in latency, energy consumption, and other physical
constraints. These limits give rise to next-generation analog neuromorhpic
hardware platforms, such as optical neural networks (ONNs), for high parallel,
low latency, and low energy computing to boost deep learning acceleration