Key Takeaways

  • FPGAs offer significantly faster performance for specific applications compared to GPUs, with advantages in latency, throughput, and energy efficiency.

  • Real-time processing and deterministic behavior make FPGAs ideal for applications like image processing, signal processing, and network optimization.

  • The cost and complexity of developing FPGA solutions remain higher than GPUs, but with advancements in design tools and cloud-based solutions, adoption is increasing.

How Much Faster is FPGA than GPU?

FPGAs (Field-Programmable Gate Arrays) and GPUs (Graphics Processing Units) are both specialized computing devices that accelerate specific tasks. However, they differ in design, capabilities, and performance characteristics. This article provides a comprehensive analysis of the performance comparison between FPGAs and GPUs, exploring their strengths and weaknesses in various applications.


Latency refers to the time it takes a device to respond to a request. FPGAs have a significant advantage over GPUs in terms of latency due to their hardware-based processing architecture. Hardware-based logic can perform operations in a fixed number of clock cycles, resulting in consistent and predictable latency. In contrast, GPUs rely on software-based execution, which can introduce variability and increase latency. For applications where real-time response is crucial, FPGAs are the preferred choice.


Throughput measures the amount of data that a device can process in a given time. While GPUs offer high throughput due to their massive number of cores, FPGAs can achieve comparable or even higher throughput for specific tasks. This is because FPGAs can be customized to optimize data flow and reduce bottlenecks. For high-performance computing applications that require massive data processing, both FPGAs and GPUs provide exceptional throughput. However, FPGAs offer an advantage in scenarios where low latency is also essential.

Energy Efficiency

Energy efficiency is an important factor in various applications, especially in embedded systems and mobile devices. FPGAs have an inherent advantage over GPUs in terms of energy efficiency due to their low power consumption. The hardware-based implementation of logic in FPGAs minimizes power dissipation, making them suitable for applications where power constraints are a concern. GPUs, on the other hand, require higher power to operate their large number of cores, which can be a limiting factor for portable and energy-sensitive devices.

Application Suitability

The suitability of FPGAs and GPUs for specific applications depends on their unique characteristics.

FPGAs are ideal for:

  • Real-time processing

  • Deterministic behavior

  • Low latency

  • High throughput

  • Power efficiency

GPUs are ideal for:

  • General-purpose computing

  • High-throughput data processing

  • Parallel computing

  • Graphics Processing


  • Image processing: FPGAs are widely used in image processing applications due to their low latency and deterministic behavior. They are used in real-time image enhancement, object detection, and image recognition.

  • Signal Processing: FPGAs are commonly found in signal processing applications, such as digital filtering, spectrum analysis, and radar signal processing. Their hardware-based implementation provides high throughput and low latency.

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