Microchip Technology machine learning involves selecting software and hardware kits, reference designs, and silicon platforms where a simplified, easy-to-use environment with advanced performance is required to build a machine-learning model.
Machine learning algorithms available from Microchip are easy to use for collecting and organising data, training neural networks, or implementing optimised edge solutions.
Mouser Electronics carries an extensive Microchip portfolio of silicon devices that includes microcontrollers (MCUs), microprocessors (MPUs), and field-programmable gate arrays (FPGAs). The software toolkits allow the use of popular machine learning frameworks, including TensorFlow, Keras, Caffe, and many others covered by the ONNX umbrella, and those found within TinyML and TensorFlow Lite.
Microchip’s EV18H79A SAMD21 Machine Learning Evaluation Kit with TDK six-axis MEMS features the SAMD21G18 Arm Cortex-M0+ based 32-bit MCU with on-board debugger, an ATECC608A CryptoAuthentication secure element IC and the fully certified ATWINC1510 Wi-Fi network controller.
Figure 1 – Microchip EV45Y33A.
The EV45Y33A SAMD21 Machine Learning Evaluation Kit from Microchip utilises the same MCU, CryptoAuthentication secure element IC and the ATWINC1510 Wi-Fi network controller, but replaces the TDK 6-axis MEMS for a BOSCH inertial measurement unit (IMU).
In addition, both kits have a Microchip MCP9808 high-accuracy temperature and a light sensor on board. Either kit will enable designers to collect data to be used for training and creating machine learning models.
Both kits provide the user with an improved development time and a local learning system which decreases the prevalence of false positives or negatives.
The combination of hardware and software in both these available kits enables users to design various applications, including high-performance AI acceleration cards, self-driving cars, security and surveillance, electronic fences, augmented and virtual reality headsets, drones, robots, satellite imagery, and communication systems.
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Figure 2 – Supervised learning flow diagram.
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