Compute Acceleration IP Cores for GlobalFoundries

Compute and acceleration IP cores are specialized hardware blocks designed to improve performance and efficiency in modern SoC and ASIC designs.

This category covers a diverse ecosystem of processing technologies, including CPU and microcontroller cores for embedded control and software execution, AI/ML accelerators for neural network processing, DSP and math engines for real-time signal processing, data compression accelerators for efficient storage and communications, and eFPGA fabrics that provide post-silicon flexibility and hardware reconfigurability.

Whether you are designing for edge AI, automotive systems, consumer electronics, or data center acceleration, you can identify the right IP to optimize your system performance.

 
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Compare 16 Compute Acceleration IP Cores for GlobalFoundries from 4 vendors

What are compute and acceleration IP cores?

Compute and acceleration IP cores are reusable hardware blocks that accelerate specific workloads in SoCs and ASICs. They are designed to handle compute-intensive tasks more efficiently than general-purpose processors.

By offloading critical functions from CPUs, these IP cores improve performance, reduce power consumption, and enable advanced features in modern electronic systems.

Main types of compute and acceleration IP

AI and Machine Learning accelerators

AI accelerator IP includes neural processing units (NPUs) and specialized hardware for running deep learning models efficiently.

DSP and signal processing IP

Digital signal processing (DSP) IP is used for audio, communications, and real-time data processing applications.

Video and image processing IP

These IP cores accelerate video encoding/decoding, image processing, and multimedia workloads.

Cryptography and security acceleration

Crypto accelerators provide high-performance encryption, hashing, and secure data processing for security-critical applications.

Reconfigurable compute (eFPGA)

eFPGA IP allows dynamic reconfiguration of hardware logic, providing flexibility for evolving workloads and applications.

Key features of compute IP cores

  • High performance for compute-intensive workloads
  • Energy efficiency compared to general-purpose CPUs
  • Specialized architectures for targeted applications
  • Scalability across different performance levels
  • Integration with system architecture

How to choose a compute or acceleration IP core

Selecting the right IP depends on your application and performance requirements:

  • Target workload (AI, DSP, video, crypto, etc.)
  • Performance requirements (throughput, latency)
  • Power and thermal constraints
  • Software ecosystem and tool support
  • Process node compatibility

Applications of compute and acceleration IP

  • AI and machine learning systems
  • Automotive and ADAS applications
  • Consumer electronics
  • Industrial automation
  • Data centers and cloud computing

Compare compute and acceleration IP cores from leading vendors

This catalog provides access to a wide range of compute and acceleration IP cores from leading providers.

Use filters to compare solutions based on performance, power efficiency, flexibility, and integration requirements.

Frequently asked questions about compute and acceleration IP cores

What is a compute IP core?

A compute IP core is a hardware block designed to accelerate specific workloads such as AI, signal processing, or cryptography in SoCs and ASICs.

Why use hardware accelerators instead of CPUs?

Hardware accelerators provide higher performance and better energy efficiency for specialized tasks compared to general-purpose CPUs.

What types of acceleration IP are available?

Common types include AI accelerators, DSP IP, video processing IP, cryptographic accelerators, and reconfigurable compute (eFPGA).

Where are compute IP cores used?

They are used in AI systems, automotive applications, consumer electronics, industrial automation, and data center environments.

How do I choose the right acceleration IP?

You should evaluate the target workload, performance requirements, power constraints, software ecosystem, and integration complexity.

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