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.

The Nano 2GB is connected and the Nano 4GB is ... [ ] in the background. Nvidia has asserted its leadership in Artificial Intelligence (AI) with a GPU architecture that continues to evolve with the growing demands of both training and inferencing AI workloads. The latest Ampere architecture provided a huge jump in performance with a new architecture that also allows the GPU to be partitioned to act as seven individual inference engines. As a result of Ampere, Nvidia's own supercomputer Selene based on the DGX A100 server ranks fifth in the TOP500 supercomputers and number one in the Green500 supercomputers. However, Nvidia is focused on more than just extreme computing as demonstrated by its proposed acquisition of Arm. Even without acquiring Arm, Nvidia has been pushing the boundaries of the AI down to lower-power and small form factor applications.

NVIDIA expanded the NVIDIA Jetson AI at the Edge platform with an entry-level developer kit priced at just $59, opening the potential of AI and robotics to a new generation of students, educators and hobbyists. The Jetson Nano 2GB Developer Kit is designed for teaching and learning AI by creating hands-on projects in such areas as robotics and intelligent IoT. To support the effort, NVIDIA also announced the availability of free online training and AI-certification programs, which will supplement the many open-source projects, how-tos and videos contributed by thousands of developers in the vibrant Jetson community. "While today's students and engineers are programming computers, in the near future they'll be interacting with, and imparting AI to, robots," said Deepu Talla, vice president and general manager of Edge Computing at NVIDIA. "The new Jetson Nano is the ultimate starter AI computer that allows hands-on learning and experimentation at an incredibly affordable price."

In this post I want to present my modest contribution to the war against the COVID19, the virus that has been changing the way we live for almost a year now. In my latest post I explained how to connect a FLIR Lepton 3 thermal camera to a NVIDIA Jetson Nano to acquire thermal images. With this post I want to explain how they can be used for an useful application for this strange period. The "Jetson Fever Control" is an application that detects the 3D position of people, calculates the body temperature of each of them and emits an alarm if the nearest one has a temperature above 37.5 C, the well know fever threshold for COVID19 screening. I added to the system a Stereolabs ZED2 3D camera to detect people and retrieve their 3D position.

While Raspberry Pi boards are great for doing all kinds of tasks and they're capable of doing object recognition, they can be a little slow when it comes to real-time image recognition. In 2019, Nvidia came out with an A.I.-focused Pi competitor in the $99 Jetson Nano. Sure, the 4GB Nano had four times as much RAM than the top-level Pi at the time, but it was more than twice as expensive and didn't come with Wi-Fi. Fast forward to 2020 and Nvidia is back with a 2GB version of the Jetson Nano that sells for a more reasonable $59 and, for consumers in some markets (including America), comes with a compatible USB Wi-Fi dongle in the box. Due out later this month, the new Nvidia Jetson Nano 2GB is designed to make A.I. more accessible to hobbyists, kids and aspiring developers. To help more hobbyists make use of its platform, Nvidia is also introducing free online training and certification programs for A.I. Since the nearly-identical 4GB Jetson Nano has been on the market for a year and a half, there's also an existing community of developers who've shared tutorials and open-source projects.