FPGA vs CPU vs GPU

Which Is Right for Your Embedded Application?

Choosing the right processing architecture can have a major impact on the performance, power consumption and flexibility of an embedded system. CPUs, GPUs and FPGAs each have different strengths, so the best choice depends on what your application needs to achieve.
CPU: Flexible and Versatile

A Central Processing Unit (CPU) is a general-purpose processor designed to handle a wide range of software tasks.

CPUs are ideal for:

  • Running operating systems and applications
  • System control and management
  • Communications
  • General-purpose processing
  • Tasks that require software flexibility

Their biggest advantage is versatility. Developers can change the functionality of a system through software without redesigning the hardware.

Choose a CPU when flexibility and general-purpose computing are your priorities.

CPU Example

GPU: Built for Parallel Processing

A Graphics Processing Unit (GPU) is designed to perform many calculations simultaneously.

While originally developed for graphics, GPUs are now widely used for:

  • Artificial intelligence
  • Machine learning
  • Computer vision
  • Image processing
  • Data-intensive workloads

GPUs can deliver excellent performance when a workload can be divided into many parallel operations.

However, power consumption, thermal requirements and latency can be important considerations in embedded applications.

Choose a GPU when your application requires high-throughput parallel processing, particularly for AI and data-intensive workloads.

GPU Example

FPGA: Customised, Real-time Processing

A Field-Programmable Gate Array (FPGA) allows engineers to configure programmable hardware logic around the requirements of an application.

FPGAs are particularly well suited to:

  • Real-time processing
  • Low-latency applications
  • High-speed data acquisition
  • Signal processing
  • Machine vision
  • Radar
  • Industrial automation
  • Hardware acceleration

Unlike a CPU or GPU, an FPGA can be configured to create dedicated processing pipelines, allowing multiple operations to happen in parallel with highly predictable timing.

Choose an FPGA when low latency, deterministic performance and specialised processing are critical.

FPGA Example

*Ratings are indicative and can vary depending on the specific hardware, architecture and workload.

Do You Need to Choose Just One?

Not necessarily.

Modern embedded systems can combine different processing architectures to take advantage of their individual strengths.

For example:

CPU → System control

FPGA → Real-time processing

GPU/AI accelerator → AI inference

This approach, known as heterogeneous computing, can provide an effective balance between performance, power efficiency and flexibility.

Sundance offers a range of embedded computing platforms that use combinations of processors and programmable logic, including FPGA modules, SoMs, MPSoC platforms and OpenVPX solutions.

Which Architecture Is Right for Your Application?

There is no single answer.

  • CPU: Best for general-purpose computing and software flexibility.
  • GPU: Best for high-throughput parallel workloads and AI.
  • FPGA: Best for real-time, low-latency and specialised processing.
  • CPU + FPGA/GPU: Ideal when an application requires multiple types of processing.

The right architecture should be selected based on your workload, latency requirements, power budget, I/O requirements and development needs.

Looking for the right embedded computing solution? Explore Sundance’s range of FPGA, CPU and heterogeneous computing platforms on the Sundance Store or contact our team to discuss your application (sales@sundance.com)

Sundance Store