Published:
Tailoring neural network models to fit on resource-constrained devices is a multi-criterion optimization problem, difficult or impossible to solve manually. Antmicro has been working with Analog Devices, Inc (ADI) to add an...
Published:
Creating Machine Learning models for deployment on constrained devices requires a considerable number of manual tweaks. Developers need to take into account the size and compute constraints of the target platform to adjust...
Published:
Kenning and Kenning Zephyr Runtime enable easy, iterative development of ML models deployed on Zephyr RTOS using various AI inference libraries. When testing models on a device with Kenning, switching between models is usually...
Published:
While the advent of Large Language Models has brought significant leaps in many areas including text analysis and computer vision, translating to benefits across a variety of industries, the size of the models can be a limitation...
Published:
For complex ML systems involving many sensors (e.g. cameras, depth sensors, accelerometers), interconnected algorithms and a sophisticated control flow, system performance monitoring is a challenging problem. To architect these...
Published:
Neural networks are a powerful tool for processing noisy and unstructured sensor data like camera frames, accelerometer or biometric readings, which makes them perfect for preprocessing or decision making on edge devices, where...
