What is Machine Learning?

Machine learning (ML) is a field of artificial intelligence that enables computer systems to learn from data, recognize patterns, and make predictions or decisions without being explicitly programmed. For developers, engineers, and architects designing embedded, mobile, or IoT systems, ML allows devices to adapt, optimize performance, and respond intelligently to real-world inputs.

Why does Machine Learning Matter?

Machine learning is transforming how data drives innovation across industries. It allows hardware and software systems to analyze information, detect trends, and act in real time. For system designers, ML enables intelligence at the edge: improving responsiveness, reducing cloud dependency, and increasing energy efficiency. In smartphones, automotive systems, medical devices, and industrial IoT, ML delivers faster insights and smarter user experiences.

How does Machine Learning Work?

Machine learning follows a general process of:

  1. Data collection and preparation: Data is gathered from sensors, logs, or datasets and formatted for analysis.
  2. Model training: Algorithms identify patterns within data to create a predictive or classification model.
  3. Inference and feedback: The model is deployed to make predictions on new data, and results are used to refine accuracy.

In embedded or on-device systems, inference often runs on specialized processors, such as Arm NPUs or GPUs, to achieve low-latency, power-efficient performance.

Key Types of Machine Learning

Machine Learning in Arm-Based Systems

Arm technology enables machine learning across a wide range of devices, from low-power sensors to data center platforms.

  • Mobile: Arm Cortex-A CPUs and Arm Mali GPUs power ML workloads for camera enhancements, voice assistants, and on-device translation.
  • IoT and embedded devices: Arm Cortex-M microcontrollers with NPUs perform predictive maintenance and anomaly detection locally.
  • Automotive and industrial systems: Arm Neoverse platforms deliver high-performance ML inference for real-time control and safety-critical applications.

By enabling intelligence where data is created, Arm helps reduce latency, preserve privacy, and optimize energy use across connected ecosystems.

Common Edge and Embedded Use Cases

  • Smartphones: Real-time photo enhancement and speech recognition run on-device for better performance and privacy.
  • Industrial IoT: ML models monitor sensors to predict equipment issues before they cause downtime.
  • Healthcare devices: Wearables use ML for heart rate monitoring and anomaly detection without cloud processing.
  • Smart homes: ML enables adaptive lighting, energy optimization, and context-aware automation.

ML Benefits for Developers and System Designers

  • Low latency: Inference at the edge ensures immediate response times.
  • Energy efficiency: Purpose-built Arm CPUs and NPUs deliver high performance per watt.
  • Scalability: ML can be deployed consistently across edge, gateway, and cloud environments.
  • Privacy and security: On-device processing reduces the need to transmit sensitive data.

Relevant Resources

Related Topics

  • AI Technology: The set of computational methods, systems, and hardware used to create, deploy, and scale artificial intelligence applications.
  • Artificial Intelligence (AI): The broader discipline of building systems that can perform tasks typically requiring human intelligence, such as reasoning, perception, and decision-making.
  • AI vs. Machine Learning: A comparison explaining how ML is a subset of AI—focused on data-driven learning—while AI encompasses a wider range of intelligent behaviors.