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Machine learning Reference Designs
Machine learning is transforming the world. Algorithms that used to work with the cloud are now scaling beyond to the edge. From surveillance to ADAS, and robotics to data centers—explore our resources to get technical expertise and stay up to date with machine learning trends and technology.
Reference Design of a smart motor driving circuit, using the RA6T2 MCU and RAA227063 3-phase smart driver for processing speed and power efficiency to address traction of the motors
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Computer Vision systems can automatically take readings from analog meters and displays, eliminating the need for manual reading and recording.
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This Renesas solution features an Automated Optical Inspection system with a Dynamically Reconfigurable Processor for AI.
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The Avnet S32V development kit is an Arm Cortex-A53 quad core based development platform for vision, machine learning, and sensor fusion applications.
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RZBoard V2L is a power efficient, vision-AI accelerated development board in popular single board computer format with well supported expansion interfaces.
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Ultra-Low Power TSN 1GbE/2.5GbE Switch with 10GbE Uplinks that meets the requirements of the new Automotive 2.0 market.
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Infineon's PSoC 6 AI Eval Kit is a hardware platform focused on Edge AI. It enables customers to evaluate the ML platform IMAGIMOB Studio, as well as ready to deploy ML models, and other SW.
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This Arduino-powered solution implements an energy monitoring-based anomaly detection system using a current sensor and machine learning models running on edge devices.
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Reference Design of an easy-to-use platform for Voice User Interface (VUI) solutions based on the RA family of 32-bit MCUs.
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This design is a proof-of-concept device which can count passengers in real-time at high speed and can give an alert if the number of people in an elevator is above a threshold.
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Small and lightweight device that can be clipped onto one's belt and worn all day without hassle, able to monitor health parameters and immediately alert assistance in case of fall, with low...
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This describes how to identify when intruders are trying to force an entrance door by analyzing surroundings sounds using a Nicla Voice, Machine Learning Tools, and the Arduino IoT Cloud.
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This design, featuring NXP's QorIQ LS1024A communications processor, provides low-power based performance scalability and flexibility for network attached storage applications.
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The QorIQ LS1046A and LS1026A processors integrate quad and dual 64-bit Arm Cortex-A72 cores respectively with packet processing acceleration and high-speed peripherals.
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The QorIQ LX2160A Reference Design Board implements the Layerscape LX2160A Multicore Communications Processor with sixteen Arm Cortex-A72 cores.
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This combination of R-Car V3H and TW8846/8847 enables these two applications' video outputs and AI processing in one system at the same time, reducing the system BOM cost.
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The Smart Application Blueprint for Rapid Engineering (SABRE) board for smart devices provides the development kit and software to evaluate NXP's i.MX 6 series of applications processors.
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Arduino Nicla Vision board can be tasked with recognizing different types of containers and sending the resulting data to the cloud automatically.
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These smart glasses are an extension of an IoT system. The system can be implemented on the glasses themselves.
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Say hello to Nicla Sense, the new sensory brain from Arduino, now built into your favorite a K-WAY jacket, powered by Edge Impulse AI, unleashing a new generation of smart clothing.
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This solution for tackling this issue revolves around using computer vision and machine learning to find small defects at the surface before automatically reporting the information.
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This reference design features the second-generation of STM32MP2 microprocessors enabling secure, advanced edge AI in Industry 4.0.
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The STM32N6570-DK Discovery kit is a complete demonstration and development platform for the Arm Cortex core based STM32N657X0H3Q microcontroller, with ST Neural-ART accelerator embed.
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This solution allows for the acquisition, process, analysis and understanding of visual images.
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Control your device with voice commands using the Arduino Speech Recognition Engine, which is a technology field that captures, interprets, and computes a voice to transform it into text (TTS).
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