Artificial Intelligence and Machine Learning Applications
Model Optimization: To run on resource-constrained embedded systems, we will see more effort put into streamlining, optimizing, and quantifying models to fit the edge computing environment.
Edge inference: Embedded devices will perform more local inference rather than sending all data to the cloud. This helps reduce latency, improve responsiveness, and reduce reliance on cloud services.
What are artificial intelligence and machine learning?
Artificial Intelligence (AI)
Definition: Artificial intelligence refers to the ability to simulate and realize human intelligence through computer systems. This includes simulating intelligent behaviors such as human perception, understanding, learning, reasoning, problem solving, and self-adaptation.
Goal: The goal of AI is to create systems that can perform a variety of intelligent tasks, which in the past were usually areas that required human intelligence, such as language understanding, image recognition, decision-making, etc.
Machine Learning (ML)
Definition: Machine learning is a method of implementing artificial intelligence that allows computer systems to learn from data and improve performance through experience without explicit programming.
Method: ML uses statistics, probability theory and optimization methods to discover patterns and rules from data through training models, which are then used to predict or make decisions on new data.
Type: Machine learning can be divided into different types such as supervised learning, unsupervised learning, and reinforcement learning, depending on the tasks and learning methods.
Edge computing enhancements
Diversity of edge devices: Different types of edge devices will be deployed on a larger scale, including sensors, embedded computing modules, edge servers, etc., to build a more complex edge computing ecosystem.
Network architecture evolution: Edge computing will promote the evolution of network architecture, adopting a more flexible, scalable and secure architecture to support distributed computing.
What is edge computing?
Edge Computing (Edge Computing) is a distributed computing paradigm. Its core idea is to bring computing, storage and network services as close as possible to data sources, users and terminal devices. , to reduce data transmission latency and bandwidth usage. Edge computing can provide faster response times, reduce network stress, and increase system reliability by performing computing tasks away from where the data is generated, rather than processing it on a remote cloud server.
Popularization of the Internet of Things (IoT)
Interconnection standards: The industry will pay more attention to the development and adoption of unified communication and interconnection standards to promote interoperability between devices.
Edge Intelligence: Embedded systems will perform more complex decisions on devices, making IoT systems more intelligent and adaptive.
What is the Internet of Things?
The Internet of Things (Internet of Things, IoT) is a system that connects and communicates through the Internet. Technology system is a concept that enables various physical devices, objects, and systems to communicate and collaborate with each other. In short, the Internet of Things integrates sensors, embedded systems, network connections and data analysis to realize interaction and information sharing between devices through the Internet.
Lower power consumption and energy efficiency
Low-power hardware: A new generation of low-power hardware design and manufacturing technologies will be widely used to extend the battery life of embedded systems.
Energy management: Design that emphasizes energy efficiency will become a core aspect of embedded system development, especially for devices that rely on battery power.
Security and Trusted Computing
Hardware security: Embedded systems will adopt more hardware-level security features, including hardware encryption, secure boot, and trusted execution environments.
Updates and vulnerability fixes: Security updates will become more automated to patch known vulnerabilities in a timely manner, and embedded systems will be more capable of protecting themselves.
The push for open source hardware and software
Community cooperation: The open source community will collaborate more closely to jointly promote hardware and software innovation and promote standardization and interoperability.
Open standards: Open standards will help eliminate monopolies in the supply chain and encourage more companies to adopt open source hardware and software to build embedded systems.
Multi-core and heterogeneous computing
Task distribution: Embedded systems will more effectively utilize multi-core and heterogeneous computing capabilities to achieve better performance and power consumption balance through task distribution.
Specialized Hardware: Specialized hardware for specific tasks will be widely used to improve performance and efficiency.
adaptive system
Environment awareness: Embedded systems will be more aware of environmental changes, adapt to different situations, and provide more intelligent and personalized services.
Learning capabilities: Some embedded systems will have learning capabilities, allowing them to optimize and adjust based on usage and feedback.
These trends are intertwined and jointly drive the future development of embedded systems. The needs of different industries and technological innovations will continue to shape the face of embedded systems, making them better adapted to changing application scenarios.