With machine learning at the edge, our devices-from smart thermostats to autonomous cars-rely on patterns and inference to learn, adapt and make decisions in real time without the latency and bandwidth challenges introduced by the cloud. NXP offers a comprehensive portfolio of MCUs and processors optimized for machine learning applications in automotive, smart industrial and IoT industries. Our software development tools enable machine learning, including toolkits for deep learning to achieve higher accuracy for safety-critical automotive applications.
Embedding Intelligence at the Edge
The potential for edge nodes to become “thinkers” makes for an explosion of smarter consumer, industrial and automotive applications. With toolkits for ML and DL, NXP solutions enable machines to learn and reason more like humans.
NXP to Expand Portfolio with Cutting Edge NPUs and AI Software
Innovative NPUs and comprehensive software enablement deliver energy-efficient AI performance across a range of neural networks, including transformer-based generative AI.
A World of Possibilities
Industrial
Devices that observe, learn and adapt in real-time can give your business better foresight and insight, informed control and new capabilities. Adding intelligence to devices can enhance performance for products across a full spectrum of commercial applications.
Consumer
ML-enabled devices such as door locks, thermostats and lights make autonomous decisions based on user preferences and real-time data for more safety, convenience and security.
Automotive
AI enablement, specifically advanced deep learning capabilities for automotive applications, will help to usher in a new era of automotive safety and intelligence.
Technology Spotlight
Introducing the eIQ® Time Series Studio: Streamlined Edge AI Development
To advance AI on the edge, we are introducing the eIQ® Time Series Studio (eIQ TSS), a new tool in our eIQ AI and machine learning development software family.
Generative AI for Embedded Applications
eIQ GenAI Flow leverages generative AI for edge use cases. Integrate RAG to provide embedded devices-specific domain knowledge without exposing user data.
i.MX RT700 Crossover MCU Designed for the AI-Enabled Edge
High performance and low power i.MX RT700 crossover MCUs for wearables, smart home, consumer, medical and HMI applications.
Great Innovation Requires a Big Toolkit
eIQ Software Environments
Inference engines, neural network compilers, optimized libraries and deep learning toolkits designed for easier and more secure system-level application development, ML algorithm enablement and auto quality ML enablement.
eIQ Neutron NPU
Neutron Neural Processing Unit (NPU) cores are a family of scalable machine learning accelerators for NXP microcontrollers and applications processors.
AI/ML Trainings
Take your technical expertise further with online and in-person instruction.
NXP's Machine Learning and Deep Learning Portfolio
MCUs and processors designed with ML and DL in mind.
Frameworks
Additional Documents
Intellectual Property Aspects of ML
This white paper sets out to add clarity to the legal background and challenges for the IP aspects of ML for data collection and model building.
Read the whitepapereIQ GenAI Flow
eIQs GenAI Flow provides building blocks like optimized Large Language Models (LLMs) and Retrieval Augmented Generation (RAG) to make generative AI applications accessible on edge devices.
Read the white paperMachine Learning at the Edge
Our ebook ‘Essentials of Edge Computing' shares insights into machine learning inference at the edge, such as uninterrupted processing, lower latency and user privacy.
Sign in to download the ebookNXP Ethical Framework for Artificial Intelligence
Given our responsibility for secure and trustworthy artificial intelligence, we at NXP aspire to uphold ethical AI principles. See how we're teaching and embedding morals into machines through robust AI ethics.
Read the whitepaper