TL;DR:
- Microchip launches the MPLAB Machine Learning Development Suite for 8-, 16-, and 32-bit MCUs and MPUs.
- It empowers embedded engineers to harness on-device machine learning, known as “tinyML.”
- The software integrates seamlessly with MPLAB X IDE, optimizing machine learning models for resource-constrained microcontrollers.
- AutoML and cloud computing resources aid in algorithm optimization.
- Comprehensive development includes feature extraction, training, validation, testing, and a Python-compatible API.
- Microchip aims to offer a complete solution for on-device machine learning.
- Licensing options range from a free trial to pro licenses with extended CPU time and source code generation.
Main AI News:
In the ever-evolving landscape of embedded controllers, Microchip continues to push the boundaries with its latest innovation: the MPLAB Machine Learning Development Suite. This comprehensive software package is poised to revolutionize the way engineers harness the potential of machine learning across 8-, 16-, and 32-bit microcontrollers and processors.
“Machine Learning is the new normal for embedded controllers, and utilizing it at the edge allows a product to be efficient, more secure, and use less power than systems that rely on cloud communication for processing,” asserts Microchip’s Rodger Richey, highlighting the core advantages of on-device machine learning, often referred to as “tinyML.” He further elaborates, “Microchip’s unique, integrated solution is designed for embedded engineers and is the first to support not just 32-bit MCUs and MPUs [Microcontroller Units and Microprocessor Units], but also 8- and 16-bit devices to enable efficient product development.”
The Synergy of Software and Hardware
At the heart of this innovation is the seamless integration with the MPLAB X Integrated Development Environment (IDE). This dynamic machine learning toolkit empowers developers to construct machine learning models tailored for Microchip’s diverse microcontroller and processor offerings. It takes into account the inherent resource constraints of these devices compared to their desktop and cloud counterparts.
Driven by AutoML and offering the flexibility to leverage cloud computing resources for optimizing algorithms, this package encompasses the full spectrum of machine learning development: from feature extraction and training to validation and testing. An intuitive application programming interface (API) that can be easily converted to Python adds another layer of convenience.
A Leap Forward in TinyML
While Microchip previously supported the use of existing deep neural network (DNN) models from TensorFlow Lite on its microcontrollers, the MPLAB Machine Learning Development Suite signifies a commitment to providing developers with everything they need to create solutions from the ground up. This launch joins the ranks of MPLAB Harmony V3 and the VectorBlox accelerator Software Development Kit (SDK), which caters to Microchip’s array of field-programmable gate array (FPGA) components, solidifying the company’s position in the realm of on-device machine learning.
Accessibility and Licensing
Accessing this cutting-edge software is a breeze: Microchip offers a free trial, allowing developers to experiment with up to 1GB of data, 2,500 labels, and five hours of AutoML CPU time each month. However, this trial doesn’t grant the rights to deploy models for anything beyond evaluation. For those ready to take their projects to the next level, a standard license is available, offering 10GB of data, unlimited labels, and 10 hours of CPU time each month, along with the ability to deploy models in production—for a modest fee of $89 per month. There’s also a “pro” license option, extending CPU time to 250 hours a year (20.8 hours per month) and providing the option to generate source code rather than relying on a pre-compiled library.
Conclusion:
Microchip’s MPLAB Machine Learning Development Suite marks a significant advancement in on-device machine learning, catering to a wide range of microcontrollers. This comprehensive solution empowers developers with the tools needed to harness the potential of machine learning, enhancing efficiency and security at the edge. It reinforces Microchip’s commitment to enabling embedded intelligence and is poised to drive innovation in the market by providing a seamless path to implement tinyML solutions.
