In this work, we evaluate the energy usage of fully embedded medical diagnosis aids based on both segmentation and classification of medical images implemented on Edge TPU and embedded GPU processors. We use glaucoma diagnosis based on color fundus images as an example to show the possibility of performing segmentation and classification in real time on embedded boards and to highlight the different energy requirements of the studied implementations. Several other works develop the use of segmentation and feature extraction techniques to detect glaucoma, among many other pathologies, with deep neural networks. Memory limitations and low processing capabilities of embedded accelerated systems (EAS) limit their use for deep network-based system training. However, including specific acceleration hardware, such as NVIDIA's Maxwell GPU or Google's Edge TPU, enables them to perform inferences using complex pre-trained networks in very reasonable times. In this study, we evaluate the timing and energy performance of two EAS equipped with Machine Learning (ML) accelerators executing an example diagnostic tool developed in a previous work. For optic disc (OD) and cup (OC) segmentation, the obtained prediction times per image are under 29 and 43 ms using Edge TPUs and Maxwell GPUs, respectively. Prediction times for the classification subsystem are lower than 10 and 14 ms for Edge TPUs and Maxwell GPUs, respectively.

Medical image segmentation can be implemented using Deep Learning methods with fast and efficient segmentation networks. Single-board computers (SBCs) are difficult to use to train deep networks due to their memory and processing limitations. Specific hardware such as Google's Edge TPU makes them suitable for real time predictions using complex pre-trained networks. In this work, we study the performance of two SBCs, with and without hardware acceleration for fundus image segmentation, though the conclusions of this study can be applied to the segmentation by deep neural networks of other types of medical images. To test the benefits of hardware acceleration, we use networks and datasets from a previous published work and generalize them by testing with a dataset with ultrasound thyroid images. We measure prediction times in both SBCs and compare them with a cloud based TPU system. The results show the feasibility of Machine Learning accelerated SBCs for optic disc and cup segmentation obtaining times below 25 milliseconds per image using Edge TPUs.

EdgeAI (Edge computing based Artificial Intelligence) has been most actively researched for the last few years to handle variety of massively distributed AI applications to meet up the strict latency requirements. Meanwhile, many companies have released edge devices with smaller form factors (low power consumption and limited resources) like the popular Raspberry Pi and Nvidia's Jetson Nano for acting as compute nodes at the edge computing environments. Although the edge devices are limited in terms of computing power and hardware resources, they are powered by accelerators to enhance their performance behavior. Therefore, it is interesting to see how AI-based Deep Neural Networks perform on such devices with limited resources. In this work, we present and compare the performance in terms of inference time and power consumption of the four Systems on a Chip (SoCs): Asus Tinker Edge R, Raspberry Pi 4, Google Coral Dev Board, Nvidia Jetson Nano, and one microcontroller: Arduino Nano 33 BLE, on different deep learning models and frameworks. We also provide a method for measuring power consumption, inference time and accuracy for the devices, which can be easily extended to other devices. Our results showcase that, for Tensorflow based quantized model, the Google Coral Dev Board delivers the best performance, both for inference time and power consumption. For a low fraction of inference computation time, i.e. less than 29.3% of the time for MobileNetV2, the Jetson Nano performs faster than the other devices.

When opportunity knocks, open the door: No one has taken heed of that adage like Nvidia, which has transformed itself from a company focused on catering to the needs of video gamers to one at the heart of the artificial-intelligence revolution. In 2001, no one predicted that the same processor architecture developed to draw realistic explosions in 3D would be just the thing to power a renaissance in deep learning. But when Nvidia realized that academics were gobbling up its graphics cards, it responded, supporting researchers with the launch of the CUDA parallel computing software framework in 2006. Since then, Nvidia has been a big player in the world of high-end embedded AI applications, where teams of highly trained (and paid) engineers have used its hardware for things like autonomous vehicles. Now the company claims to be making it easy for even hobbyists to use embedded machine learning, with its US $100 Jetson Nano dev kit, which was originally launched in early 2019 and rereleased this March with several upgrades.

By Sciforce, software solutions based on science-driven information technologies. One of the major IoT trends for 2019 that are constantly mentioned in ratings and articles is edge analytics. It is considered to be the future of sensor handling, and it is already, at least in some cases, preferred over usual clouds. First of all, let's go deeper into the idea. Edge analytics refers to an approach to data collection and analysis in which an automated analytical computation is performed on data at a sensor, network switch, or another device instead of sending the data back to a centralized data store. What this means is that data collection, processing, and analysis are performed on-site at the edge of a network in real-time.

Supermarkets need to ensure clean and safe environments for both shoppers and employees. Slips, trips, and falls can result in injuries that have a physical as well as financial cost. Timely detection of hazardous conditions such as spilled liquids or fallen items on supermarket floors can reduce the chances of serious injuries. This paper presents EdgeLite, a novel, lightweight deep learning model for easy deployment and inference on resource-constrained devices. We describe the use of EdgeLite on two edge devices for detecting supermarket floor hazards. On a hazard detection dataset that we developed, EdgeLite, when deployed on edge devices, outperformed six state-of-the-art object detection models in terms of accuracy while having comparable memory usage and inference time.

Last March, Google took the wraps off of Coral, a collection of hardware development kits and accessories intended to bolster the development of machine learning models at the edge. It launched in select regions in beta, but the tech giant today announced that it's graduating to a "wider" and global release. All Coral products -- including the $150 Coral Dev Board, the $74.99 Coral USB Accelerator, and the $24.99 5-megapixel camera accessory -- are available for sale at electronics retailer Mouser and for large-volume sale through Google's sales team. The company says that by the end of the year, it'll expand distribution into new markets including Taiwan, Australia, New Zealand, India, Thailand, Singapore, Oman, Ghana, and the Philippines. Coinciding with Coral's general availability, the Coral website -- which now lives at Coral.ai -- has been revamped with better organization for docs and tools, testimonials, and "industry-focused" pages. Additionally, it links to a new set of examples aimed at providing solutions to common AI problems, such as image classification, object detection, pose estimation, and keyword spotting.

Google revolutionised the way the world uses the internet with its landmark PageRank algorithm. Today, after two decades, Google has grown into an AI powerhouse that generates state-of-the-art algorithms that touch almost every domain known to mankind. As Google turns 21, we have compiled a list of 21 notable contributions from Google which has enriched the machine learning community across the globe. The core open source library to help you develop and train ML models developed by the team at Google Brain. TensorFlow's machine learning platform has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

The rise of industrial Internet of Things (IoT) and artificial intelligence (AI) are making edge computing significant for enterprises. Many industry verticals such as manufacturing, healthcare, automobile, transportation, and aviation are considering an investment in edge computing. Edge computing is fast becoming the conduit between the devices that generate data and the public cloud that processes the data. In the context of machine learning and artificial intelligence, the public cloud is used for training the models and the edge is utilized for inferencing. To accelerate ML training in the cloud, public cloud vendors such as AWS, Azure, and the Google Cloud Platform (GCP) offer GPU-backed virtual machines.

These new devices are made by Coral, Google's new platform for enabling embedded developers to build amazing experiences with local AI. Coral's first products are powered by Google's Edge TPU chip, and are purpose-built to run TensorFlow Lite, TensorFlow's lightweight solution for mobile and embedded devices. As a developer, you can use Coral devices to explore and prototype new applications for on-device machine learning inference. Coral's Dev Board is a single-board Linux computer with a removable System-On-Module (SOM) hosting the Edge TPU. It allows you to prototype applications and then scale to production by including the SOM in your own devices.