Training on edge devices poses several challenges as these devices are generally resource-constrained, especially in terms of power. State-of-the-art techniques at the device level reduce the GPU frequency to enforce power constraints, leading to a significant increase in training time. To accelerate training, we propose to jointly adjust the system and application parameters (in our case, the GPU frequency and the batch size of the training task) while adhering to the power constraints on devices. We introduce a novel cross-layer methodology that combines predictions of batch size efficiency and device profiling to achieve the desired optimization. Our evaluation on real hardware shows that our method outperforms the current baselines that depend on state of the art techniques, reducing the training time by $2.4\times$ with results very close to optimal. Our measurements also indicate a substantial reduction in the overall energy used for the training process. These gains are achieved without reduction in the performance of the trained model.

The proliferation of complex deep learning (DL) models has revolutionized various applications, including computer vision-based solutions, prompting their integration into real-time systems. However, the resource-intensive nature of these models poses challenges for deployment on low-computational power and low-memory devices, like embedded and edge devices. This work empirically investigates the optimization of such complex DL models to analyze their functionality on an embedded device, particularly on the NVIDIA Jetson Nano. It evaluates the effectiveness of the optimized models in terms of their inference speed for image classification and video action detection. The experimental results reveal that, on average, optimized models exhibit a 16.11% speed improvement over their non-optimized counterparts. This not only emphasizes the critical need to consider hardware constraints and environmental sustainability in model development and deployment but also underscores the pivotal role of model optimization in enabling the widespread deployment of AI-assisted technologies on resource-constrained computational systems. It also serves as proof that prioritizing hardware-specific model optimization leads to efficient and scalable solutions that substantially decrease energy consumption and carbon footprint.

Haar Cascade is a cost-effective and user-friendly machine learning-based algorithm for detecting objects in images and videos. Unlike Deep Learning algorithms, which typically require significant resources and expensive computing costs, it uses simple image processing techniques like edge detection and Haar features that are easy to comprehend and implement. By combining Haar Cascade with OpenCV2 on an embedded computer like the NVIDIA Jetson Nano, this system can accurately detect and match faces in a database for attendance tracking. This system aims to achieve several specific objectives that set it apart from existing solutions. It leverages Haar Cascade, enriched with carefully selected Haar features, such as Haar-like wavelets, and employs advanced edge detection techniques. These techniques enable precise face detection and matching in both images and videos, contributing to high accuracy and robust performance. By doing so, it minimizes manual intervention and reduces errors, thereby strengthening accountability. Additionally, the integration of OpenCV2 and the NVIDIA Jetson Nano optimizes processing efficiency, making it suitable for resource-constrained environments. This system caters to a diverse range of educational institutions, including schools, colleges, vocational training centers, and various workplace settings such as small businesses, offices, and factories. ... The system's affordability and efficiency democratize attendance management technology, making it accessible to a broader audience. Consequently, it has the potential to transform attendance tracking and management practices, ultimately leading to heightened productivity and accountability. In conclusion, this system represents a groundbreaking approach to attendance tracking and management...

Satellites have become more widely available due to the reduction in size and cost of their components. As a result, there has been an advent of smaller organizations having the ability to deploy satellites with a variety of data-intensive applications to run on them. One popular application is image analysis to detect, for example, land, ice, clouds, etc. for Earth observation. However, the resource-constrained nature of the devices deployed in satellites creates additional challenges for this resource-intensive application. In this paper, we present our work and lessons-learned on building an Image Processing Unit (IPU) for a satellite. We first investigate the performance of a variety of edge devices (comparing CPU, GPU, TPU, and VPU) for deep-learning-based image processing on satellites. Our goal is to identify devices that can achieve accurate results and are flexible when workload changes while satisfying the power and latency constraints of satellites. Our results demonstrate that hardware accelerators such as ASICs and GPUs are essential for meeting the latency requirements. However, state-of-the-art edge devices with GPUs may draw too much power for deployment on a satellite. Then, we use the findings gained from the performance analysis to guide the development of the IPU module for an upcoming satellite mission. We detail how to integrate such a module into an existing satellite architecture and the software necessary to support various missions utilizing this module.

We identify and develop three separate critical components. This includes a Unity simulation and reinforcement learning model training pipeline, a malleable computer vision pipeline, and a data transfer pipeline to offload large computations from the VEX V5 Brain/micro-controller to an external computer. We give the community access to all of these components in hopes they can reuse and improve upon them in the future, and that it'll spark new ideas for autonomy as well as the necessary infrastructure and programs for AI in educational robotics.

Deep Learning #ai with Nvidia Jetson Nano #nvidia #deeplearning...

We are using NVIDIA Jetson Nano Developer kit as our accelerator system.Which will contain Docker Container which will contain the dataset and trained model SSD (Single Shot Detector) MobileNetV2 which we will be used to for facial detection. Video would be recorded through the Camera attached to the accelerator system. Code of the SSD (Single Shot Detector) MobileNetV2 is written in Python Programming Language and Deep learning framework which has been used is PyTorch.To optimized the neural network layers.NVIDIA TensorRT is used for faster Inference during the run time.

In this tutorial you will learn how to use my pre-configured NVIDIA Jetson Nano .img If you've ever configured an NVIDIA product such as the TX1, TX2, and even the Nano, you know that working with NVIDIA's Jetpack and installing libraries is far from straightforward. It is developed and supported by my team here at PyImageSearch to save you time and bring you up to speed quickly for developing your own embedded CV/DL projects and for following along with my new book Raspberry Pi for Computer Vision. If you purchase a copy of the Complete Bundle of Raspberry Pi for Computer Vision, you'll gain access to this accompanying .img. All you have to do is (1) download the .img

Have you thought about adding artificial intelligence to your project? It's easier than you think, and NVIDIA is making AI fully accessible to makers, self-taught developers, and embedded technology enthusiasts by providing the tools and inspiration to get up and running fast. In this contest, you'll tap into the power of the new NVIDIA Jetson Nano - a powerful, easy-to-use, mini AI computer that lets you run multiple neural networks in parallel. It's perfect for any project using image classification, object detection, segmentation, speech processing, and more. NVIDIA is giving away thousands in prizes including a paid trip to NVIDIA GTC 2020, a brand new Titan RTX, a Jetson AGX Xavier Developer Kit, and Public Cloud Compute credits that you can use for your next project.

The SparkFun JetBot AI Kit is a robot platform powered by the Jetson Nano Developer Kit by NVIDIA. This SparkFun kit is based on the open-source NVIDIA JetBot! We understand that not everyone has access to multiple 3D printers on each floor, and a whole warehouse of electronics so we wanted to build a kit from ready to assemble parts to get you up and running as quickly as possible. The SparkFun JetBot AI Kit is a great launchpad for creating entirely new AI projects for makers, students and enthusiasts who are interested in learning AI and building fun applications. It's straightforward to set up and use and is compatible with many popular accessories.