In the rapidly evolving world of embedded systems, it can be crucial to choose the right hardware accelerator for a successful project. Two of the most common technologies used to increase performances in demanding applications are Field-Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs). Both technologies offer unique advantages. But their suitability highly depends on the specific requirements of the project. While GPUs excel in parallel processing tasks such as image recognition and deep learning, FPGAs provide ever unmatched flexibility and customization, making them ideal for specialized applications.
What is an FPGA?
FPGA = Field-Programmable Gate Array
An FPGA is an integrated circuit that can be configured by the user after the development. Unlike GPUs (designed with a fixed architecture for parallel processing tasks), FPGAs offer reprogrammability. It allows their internal configuration to be altered multiple times to meet evolving project requirements or to fix issues. The core structure of an FPGA is made of programmable logic blocks interconnected through flexible routing. This structure enables the creation of highly customized circuits. This adaptability makes FPGAs ideal for rapid prototyping and applications requiring frequent updates or modifications. Additionally, FPGAs contribute to faster development cycles since they bypass the extensive design and production processes needed for fixed-function hardware like GPUs. However, this flexibility has trade-offs: FPGAs generally have a higher per-unit cost in large-scale production and tend to consume more power than GPUs in certain workloads. FPGAs are especially favored in research, development, and low-volume products, where flexibility and quick iterations are crucial.
What is a GPU?
GPU = Graphics Processing Unit
A Graphics Processing Unit is an hardware component designed to accelerate the rendering of images and graphics. Unlike a CPU (central processing unit), optimized for general-purpose tasks, a GPU is built to handle parallel processing. It is particularly effective for tasks that involves complex calculations, such as 3D rendering, image processing and deep learning. GPUs are made of thousands of small cores that work at the same time to execute multiple threads. This means shorter calculation times in applications such as video games, artificial intelligence and scientific simulations. With the growing demand for high-performance processing capabilities, GPUs have become essential in a variety of areas, including gaming, machine learning and data analysis.
Differences between FPGAs and GPUs?
FPGAs (Field-Programmable Gate Arrays) and GPUs (Graphics Processing Units) totally differ in their architecture and use, even though it is two types of hardware accelerators used to improve the performance of certain applications. FPGAs are reprogrammable and allow a full customization, offering maximum flexibility for the creation of tailor-made solutions. This makes it ideal for projects requiring frequent updates or evolving specifications. GPUs, on the other hand, are optimized for massive parallel processing and are particularly effective in tasks such as deep learning, graphics rendering or image processing.
While FPGAs are recommended for low latency and applications with hardware customization, GPUs are better for quickly process large amounts of data, such as in HPC applications.
Using an FPGA, using a GPU
When should you choose an FPGA?
FPGAs are highly beneficial for projects that demand flexibility and a fast development cycle. In areas like prototyping and the creation of new products, they allow engineers to swiftly experiment with different configurations and make real-time modifications. This capability is especially valuable in research and development, where project requirements may evolve over time. Additionally, FPGAs are commonly found in testing and measurement systems, thanks to their reprogrammable nature, which allows them to perform a variety of tasks. In sectors such as military and aerospace, where both resilience and the ability to reconfigure hardware are critical, FPGAs provide a versatile solution that can adapt to shifting needs and challenging environments. Lastly, for products with limited production volumes—such as specialized medical devices or niche communication systems—FPGAs offer a flexible and cost-efficient alternative.
When should you choose a GPU?
GPUs are more appropriate to choose if your project requires significant computing power for parallelized tasks, such as deep learning, graphics rendering or image processing. Their architecture, optimized for parallel processing, makes them the ideal choice for projects requiring speed, efficiency and management of large volumes of data.
The choice between FPGAs and GPUs therefore depends on the specific needs of your project, such as the level of flexibility required, power consumption constraints, processing speed and task type – whether you prioritize custom hardware configurations and low-latency processing with FPGAs, or high-performance parallel computing and data throughput with GPUs.
Do not hesitate to contact our teams so that we can analyze your FPGA requirements and provide you with the best possible guidance.