AI On The Edge: Embedded Computing Devices for ML
Artificial Intelligence (AI) and Machine Learning (ML) have seen remarkable advancements in recent years, and one of the key enablers of this progress has been the development of embedded computing devices tailored for AI and ML applications. These devices bring the power of machine learning to the edge, offering faster, more efficient, and real-time processing capabilities.
In this article, we will explore some notable embedded computing devices, including the Jetson Nano, OAK-D, Google Coral Dev Board Micro, and Raspberry Pi, and discuss their significance in revolutionizing AI and ML applications.
Jetson Nano
The NVIDIA Jetson Nano is a compact, low-cost embedded computing device designed for AI and ML tasks. It features a quad-core ARM Cortex-A57 CPU and a 128-core Maxwell GPU, making it ideal for running deep learning models. The Jetson Nano is commonly used in robotics, edge AI, and IoT applications, where real-time inference is crucial.
OAK-D: AI Stereo Camera
The OpenCV AI Kit Depth (OAK-D) is an AI-powered stereo camera that combines depth perception and AI inferencing in a single device. It’s equipped with a Myriad X VPU for on-device neural network acceleration, making it capable of tasks like object detection, depth estimation, and more. OAK-D is a valuable tool for robotics, autonomous navigation, and computer vision applications.
Google Coral Dev Board Micro: Tiny AI Powerhouse
The Google Coral Dev Board Micro is a pocket-sized AI development board that features Google’s Edge TPU (Tensor Processing Unit). This tiny yet powerful board can execute complex ML models with low latency and is well-suited for applications like image and speech recognition, as well as edge AI deployments.
Raspberry Pi
While the Raspberry Pi was initially designed for educational purposes, it has evolved into a versatile platform for AI experimentation and deployment. With models like the Raspberry Pi 4 and Compute Module 4, users can harness the computational power of quad-core ARM processors and GPU acceleration to run machine learning models efficiently.
The Significance of Embedded Machine Learning
Almost every application I discuss has an element of embedded machine learning. So what makes these so important, and how are they transforming a wide range of industries and applications?
- Edge Computing: By bringing AI capabilities closer to data sources, embedded devices enable real-time decision-making in fields like autonomous vehicles, healthcare, and industrial automation.
- Privacy: Edge AI devices process data locally, reducing the need for sending sensitive information to the cloud, thereby enhancing privacy and security.
- Efficiency: Embedded devices are energy-efficient and can operate in resource-constrained environments, making them ideal for battery-powered or remote applications.
- Cost-Effectiveness: Many embedded AI devices are affordable, democratizing access to AI technologies and making them accessible to startups and hobbyists.
Embedded computing devices for AI and ML are paving the way for a new era of intelligent, edge-driven applications. These devices empower developers and businesses to leverage the full potential of AI and ML in real-time, resource-efficient scenarios. As technology continues to advance, we can expect even more compact, powerful, and accessible embedded solutions that will further revolutionize the way we interact with AI on the edge.
