Machine Learning & Raspberry Pi
Machine learning embedded devices are increasingly common and affordable. For coders and IT enthusiasts, the combination of Raspberry Pi with machine learning is particularly intriguing. The Raspberry Pi 4's enhanced capabilities make it an excellent platform for exploring AI and ML applications.
These models operate on Raspberry Pi and access essential AI tools through cloud services. The Raspberry Pi serves as a conduit between physical computing hardware and AI models, converting them into useful real-world projects.
Raspberry Pi 4 Capabilities
The latest Raspberry Pi 4 can achieve an additional 300MHz of stock clock speed through custom overclocking using either the fan in the official Power over Ethernet (PoE) HAT or a third-party cooler. The Pi 4 can use its integrated camera input for image recognition to execute simple machine learning algorithms.
Key advantages include:
- Raspberry Pi's Arm Cortex-A53 quad-core processor offers performance potential through single instruction multiple data (SIMD) extensions
- NEON extensions enable efficient processing of certain ML types
- Compatible and easily accessible hardware add-ons expand the base platform
- Performs object recognition, motion detection, and simple inference tasks
- Faster CPU and RAM enable superior load and run-time algorithms
- Machine learning tasks perform twice as well as earlier models
Getting Started with Google Alto
Google created Alto, a reasonably priced "teachable object" that makes machine learning accessible to makers. Alto is a delightful little robot-like device developed to expose makers to machine learning through inexpensive materials and a streamlined toolchain.
What's Inside Alto
Alto's cardboard enclosure includes a Coral USB Accelerator and a Raspberry Pi Zero W single-board computer. The Coral USB Accelerator adds an Edge TPU to substantially speed up and enhance the performance of machine learning models created using Google's TensorFlow.
The default machine learning model for Alto manages object recognition. It views objects with a Raspberry Pi camera and attempts to identify them by referencing previously learned photos. If it recognizes an object, it will raise an arm to point at it. It can also learn to recognize new objects when shown them.
Real-World Applications
The Raspberry Pi single-board computer has captured the enthusiasm of DIY enthusiasts and amateurs worldwide. Here are practical applications combining Raspberry Pi with machine learning:
File Storage Server
Your Raspberry Pi can act as a central hub for household equipment. By connecting an external USB hard drive, you can use it as a low-cost NAS system for general file storage. Switch from Wi-Fi to a physical ethernet connection for optimal file server performance.
Music Streaming Machine
Several operating systems turn the Pi into a high-fidelity music player when coupled with speakers, similar to a more robust, adaptable Chromecast Audio.
Twitter Bot
Create a bot to send Tweets on your behalf using Twitter's APIs. Python programming with the Twython library enables this popular Raspberry Pi project.
Retro Gaming Console
Relive classic gaming by converting your Raspberry Pi board into a retro game console. Install RetroPie and control it with a USB controller from PS3, PS4, or Xbox 360.