AI is a term that is thrown around pretty often nowadays and for good reason. This technology is transforming lives and is very marketable. So if you are an AI engineer or thinking about getting into this field, you need an AI development board to perfect your skills. Let’s look at what these development boards are, why you need them, their use cases, and how you can get them made to suit your specifications at low costs.
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What Are AI Development Boards?
AI dev boards are specialized hardware platforms that are designed to facilitate the development, testing, and deployment of AI and ML applications. These devices are usually single-board computers with dedicated processing units (usually GPUs) that are optimized for neural network computations and accelerated AI training plus inference. The key feature is that they provide sufficient power to develop, prototype, train, and infer AI models without needing the cloud. Additionally, they consume very little energy.
So essentially, these dev boards are edge computing devices that enable real-time processing and AI-based decision-making at the network’s edge, which is after prototyping and development on the same computer.
Some of the popular AI development boards you can buy off the shelf are the NVIDIA Jetson series of computers, Sipeed AI boards, Huskylens, and Google Coral boards.
Why You Need an AI Development Board
For Experimentation
Most breakthroughs are born out of experimentation or accidental discoveries. In AI development, experimentation means developing and trying out multiple AI models with their accompanying data sets to solve certain problems. Case in point is the prediction of protein structures, which has helped to design novel proteins that are used to develop new vaccines and enzymes in medicine.
AI dev kits enable rapid prototyping and development of these prediction models, making it easier to solve various problems in the medical, engineering, and theoretical physics domains.
For AI Training and Prediction (Inference)
AI development boards feature specialized AI accelerators or GPUs as part of their hardware arsenal, which speed up the training of ML models and the prediction they make afterward to assess their correctness.
To Leverage Their Accompanying Software Tools
In addition to the hardware, AI dev boards come with software tools like SDKs, their libraries, APIs, and frameworks, which help you build, test, debug, and deploy your AI-powered applications.
To Accelerate AI Development
Since they provide a specialized hardware and software platform for AI development, these boards reduce the time needed to develop AI-powered applications before large-scale deployment.
For Developing Embedded Systems
AI dev boards are usually compact and energy-efficient single-board computers, which makes them ideal for building AI-powered embedded systems to perform real-time decision-making in devices like robots, drones, and IoT hardware.
For Edge Computing
Since they carry with them the required AI computing power, AI development boards are critical for developing AI-powered decision-making applications closer to the data source. This can be on surveillance cameras, self-driving cars, smart farming equipment, and intelligent energy grids.
To Cut AI Development Costs
AI dev kits are sometimes the most cost-effective alternatives when compared to cloud AI development, which requires payment of subscription fees. The one-time cost of buying the board is usually cheaper in the long run. This option is also more viable if you want to specialize in developing AI solutions for edge computing and embedded systems.
Popular Use Cases of AI Development Boards
IoT
IoT traditionally relied on cloud-sensor integration, which presented latency and network issues. But with AI integration at the edge, the smart nodes can now process data and make decisions locally at the source of data, reducing reliance on the network and making the concept of real-time operation more practical.
Simple tasks like voice detection and processing using AI development boards at the edge enable offline voice assistants to control home appliances in smart homes, such as thermostats, robot vacuums, and lights faster.
Image Recognition
Perhaps the most commonly used aspect of AI is image recognition for applications like facial recognition in surveillance cameras, object detection in autonomous vehicles, and industrial inspection of finished products before packaging for quality control.
Robotics
Robots also use image recognition for navigation, but they add on object manipulation (if they have arms) and speech recognition if they are given verbal commands.
Industrial Automation
Industrial automation covers predictive maintenance, quality control, supply chain optimization, automated assembly lines, digital twins, etc. Companies like OurPCB have automated their PCB assembly lines using AI, which has improved yield quality and increased production speeds. This AI automation is usually done as an IoT system with edge computing nodes.
Conclusion
AI is revolutionizing almost every industry in the world, and it all begins with the development of AI models for training and inference testing. So if you want to get into the race for developing the next big AI application or feature, it is necessary to have an AI development board, which you can buy off the shelf or get one custom-made using OurPCB. This company specializes in fabricating and assembling custom PCBs for different applications, including PCBs, so you can get a board preassembled with all the components, including your preferred AI accelerator. Contact them for more information about their pricing and PCB customization options.
