What is a real-time workload?

This is a compute workload that needs to be run within predictable and specific time constraints. Such workloads may be part of many applications spanning from process automation to production assembly lines, robotics, transportation systems, healthcare, and live performances. A few examples of real-time workloads include: A real-time controller driving a manufacturing process. A control system within a vehicle or aircraft. A real-time audio/video mixing application that's part of a live performance. Real-time workloads may also include AI components such as real-time data fusion of multiple sensors in an autonomous robot or vehicle used as input to safety and time-critical decisions.

Intel® Time Coordinated Computing and AI at the Edge

Overview

A major transformation towards software-defined solutions and AI is happening at the edge. It's driven by the need for flexibility and scalability, changes in the workforce, automation, advances in edge-based AI, and more. A key element of this transformation is the capability to support real-time workloads along with best-effort workloads (mixed-criticality) on the same system. Moreover, emerging software-defined systems and AI applications are distributed, involving multiple computing nodes (for example, multiple sensors, controllers, and actuators) that perform coordinated actions based on a shared understanding of time. This distributed computing paradigm, Time Coordinated Computing, is crucial across edge applications, including real-time systems, industrial automation, distributed displays, large-scale simulations, and digital twins using edge AI.

Edge platforms from Intel include optimized hardware capabilities and a strong software ecosystem to support mixed-criticality workloads and ensure real-time performance, delivering a notable Time Coordinated Computing experience.

Intel Time Coordinated Computing Experience at the Edge

Edge platforms from Intel deliver a notable Intel® Time Coordinated Computing (Intel® TCC) experience with high-performance compute and real-time guarantees by:

Prioritizing real-time workloads access to cache, memory, and networking resources
Minimizing disruption from other workloads
Optimizing performance for both real-time and non-real-time workloads
Supporting availability in both native and virtualized environments
Providing precise and synchronized timing within the platform and across wired and wireless networks
Offering support by a large software ecosystem and standards-based interoperable connectivity

What Is Intel TCC?

Intel® TCC refers to the comprehensive set of optimizations throughout the platform and a network stack from Intel silicon to network interface cards that enable a notable TCC experience for our customers that includes:

Silicon and platform software optimizations.
End-point connectivity to support IEEE time-sensitive networking (TSN) standards that reduces latencies and improves clock synchronization for real-time workloads distributed across wired and wireless networks.

When specific real-time workloads must be completed within a set deadline to avoid critical system failure, the ability to prioritize some workloads over others is paramount.

Real-time workloads process data in defined, predictable time frames, enhancing the reliability of safety and time-critical systems.

Real-time systems and time-aware applications are designed to perform tasks at precise times, within strict deadlines (down to microseconds).

Applications and distributed systems can coordinate actions across wired and wireless networks with high precision and determinism.

FAQs

Frequently Asked Questions

This is a compute workload that needs to be run within predictable and specific time constraints. Such workloads may be part of many applications spanning from process automation to production assembly lines, robotics, transportation systems, healthcare, and live performances. A few examples of real-time workloads include:

A real-time controller driving a manufacturing process.
A control system within a vehicle or aircraft.
A real-time audio/video mixing application that's part of a live performance.

Real-time workloads may also include AI components such as real-time data fusion of multiple sensors in an autonomous robot or vehicle used as input to safety and time-critical decisions.

A mixed-criticality environment refers to a computing and networking environment that can run multiple workloads with different performance requirements. Some workloads may have time-critical performance constraints, and other workloads may only need best-effort performance. As more and more systems and applications transition from dedicated hardware to software-defined systems, the need to support mixed-criticality workloads on the same general-purpose compute platform and network is becoming a fundamental requirement across multiple markets.

Intel TCC includes several optimizations to ensure real-time performance in a mixed-criticality computing and networking environment. Some of the capabilities and optimizations include: precision time coordination, power-state transition optimizations, memory/cache allocation, interrupt request (IRQ) optimizations, virtual channels, Intel® Speed Shift Technology for edge compute (enables assignment of processor performance as needed), and support for TSN.

Key Use Cases

Edge AI

Flight Control

Industrial Controls Applications

Video Wall

Real-Time Audio & Video Processing

Medical Imaging

Robotics

Key Elements of the Intel TCC Experience

As a part of the Intel TCC experience, key elements enabled by edge platforms from Intel include:

Clock Synchronization: Ensures that all participating systems have a consistent and accurate sense of time. This is typically achieved through protocols that synchronize the clocks of different systems to a common reference time.
Temporal Coordination: Uses the synchronized clocks to schedule and run tasks at specific times. This can involve triggering events, starting computations, or exchanging data at predetermined times.
Deterministic Execution: Ensures that the computation outcomes are predictable and repeatable, which is essential for applications that rely on precise timing.
Latency Management: Minimizes and manages computation and communication delays between systems to ensure that time-sensitive operations are performed accurately.

Overall, Intel TCC enables systems to work together more effectively by providing a common temporal framework, which is essential for achieving high levels of coordination and performance in distributed environments.

Enhancements for Edge AI

When it comes to edge AI applications, Intel TCC optimizations can be used in several ways:

Protecting real-time workloads from interference: As more AI workloads are introduced, it is important to ensure that the performance of real-time workloads is not impacted by AI workloads executing on CPUs, GPUs, and NPUs. Intel TCC optimizations can protect the execution of real-time workloads. For instance, it is possible to reserve cores and cache for real-time workloads to ensure reliable execution with low latency and jitter while controlling the amount of cache available to GPUs to avoid contention.
Enhancing edge AI applications that need real-time performance: TCC optimizations can also be used by edge AI applications that need real-time performance by using precise timing, efficient resource allocation, and precision time coordination across edge devices. Some of the additional Intel TCC benefits for edge AI applications include:
- Synchronized Operations: All edge devices can operate on a synchronized timeline, which is crucial for applications requiring coordinated actions in real time. For example, in a smart factory, synchronized robots and sensors can work together seamlessly to optimize production processes
- Real-Time Data Fusion: By aligning the timing of data collection and processing across multiple edge devices, Intel TCC enables AI-based data fusion from various sources to use a common time reference. This is essential for applications like autonomous mobile robots and vehicles, where data from cameras and other sensors must be combined in real time to make safe driving decisions.
- Improved Reliability and Consistency: With Intel TCC, edge devices can perform deterministic AI-driven operations, ensuring predictable and consistent outcomes. This reliability is vital for applications such as industrial automation, where precise timing can prevent errors and improve efficiency.
- Efficient Resource Management: Intel TCC helps optimize computational resources by coordinating AI and other tasks within a platform used for mixed-criticality workloads. This can lead to better efficiency and utilization of general-purpose computing resources in mixed-criticality environments.

TSN

This technology provides the capabilities to achieve high-precision clock synchronization and bounded latency in converged networks that can be shared by real-time and best-effort applications.

TSN is a set of IEEE standards that are defined by the IEEE 802.1 TSN task group. These standards enhance standard connectivity solutions, such as Ethernet, to enable ultra-reliable, low-latency communication to support time-critical applications. TSN tools include time synchronization protocols, traffic scheduling, and network configuration enhancements. TSN capabilities originally designed for Ethernet networks have also been extended to operate over Wi-Fi* and 5G systems.

Why TSN?

More secure and reliable delivery of data
Bounded latency for time-critical data delivery
Converged networks save operating costs
Simplifies system configuration and operation
Open ecosystem that is based on IEEE industry standards and the Avnu Alliance certification program
Support and platform integration of TSN capable Ethernet, Wi-Fi, and 5G connectivity
Enable ultra-reliable low-latency communication (URLLC) machine-to-machine communication
Supports coexistence of hard real-time operational technology (OT) and soft real-time information technology traffic in a single network, reducing infrastructure cost
Streamline software development with open-standard reference software

Supported TSN Standards and Certification

IEEE 802.1AS Generalized Precision Time Protocol: Supports the distribution of time across the network with high accuracy.
IEEE 802.1Qbv Time-Aware Shaper: Controls latency by scheduling transmissions to avoid congestion.
IEEE 802.1Qav Credit-Based Shaper: Controls bandwidth and latency based on accumulated credits.
IEEE 802.1Qbu Frame Preemption: Enables express service for time-critical data.

Certification for TSN components, network devices, and market-specific profiles is provided by Avnu Alliance.

Intel® Ethernet Controller i226 Series Is Certified for TSN Components.

Development and Testing Resources

Intel works with the broader community to enable a strong software ecosystem to take advantage of platform optimizations and support the Intel Time Coordinated Computing experience. Developers and users who build applications that need real-time performance can use the following documentation:

TCC Tutorial: A guide on optimizing Linux* real-time performance on Intel CPUs is available on GitHub*.
Canonical* Guide to Real-Time Linux: Recommended reading as an introduction to Linux real-time capabilities.
Real-Time Ubuntu* Documentation: Optimizing real-time performance on Intel CPUs is a collaborative tutorial between Intel and Canonical.
Linux Real-Time Communication Testbench: An open source tool for validating and testing real-time applications and TSN capabilities on Linux systems is available on GitHub.
TSN Reference Applications: A set of TSN samples and reference applications from Intel is available on GitHub.

Learn More

Intel TCC User Guide: Guidance for Using Intel TCC on Intel Platforms

The Business Impact of TSN for Industrial Systems

Featured Platforms

For a list of edge computing offerings at Intel, see Embedded Processors.

Real-Time Ecosystem Highlights

Partner Enabling

Customer and Partner Success Stories

Real-Time Solutions Hypervisor Video

"The introduction of the RTS Hypervisor on the latest Intel platforms facilitates new possibilities for real-time applications ranging from robotics and smart cars to factory automation and utility load management."

— Michael Reichlin, general manager of Real-Time Systems

Wind River VxWorks* Operating System: Real-Time and Functional Safety Solutions on Intel-Based Industrial Platforms

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