Graphics processing units (GPUs) and field programmable gate arrays (FPGAs) are two of the three main processor types for imaging and other heavy calculations. Central processing units (CPUs) are the third type. Let's dig into the key differences between GPUs and FPGAs, their advantages, common use cases, and when to choose one over the other.
What is an FPGA?
An FPGA (field-programmable gate array) is an integrated circuit with a programmable hardware fabric that allows it to be reconfigured to behave like another circuit. Because its circuitry is not hard-etched, it can flexibly adapt to the needs of a specific machine learning algorithm. In the context of AI, this provides huge advantages in the ability of an FPGA to both support massively parallel workloads and boost the performance of a particular algorithm.
Choosing between GPUs and FPGAs
The main difference between GPUs and FPGAs is that GPUs were originally designed to render video and graphics. Their ability to handle workloads in parallel made them popular for deep learning applications for situations where the same workload needs to be performed many times at speed. For image recognition tasks, for example, GPUs are a natural choice.
FPGAs, on the other hand have the flexibility to be programmed to function as a GPU, ASIC, or other configurations. They can be programmed and optimized for specific algorithms, making them highly efficient in scenarios where general-purpose hardware might not be sufficient.
GPUs and the power of parallel processing
The greatest strength of GPUs is their ability to render graphics. From rendering high resolution images and animations to handling the complex calculations behind ray tracing, they are perfect for interfacing with displays and handling the computations needed to render scenes with high resolutions and detailed textures.
Editor's Note:
This guest blog post was written by the staff at Pure Storage, an US-based publicly traded tech company dedicated to enterprise all-flash data storage solutions. Pure Storage keeps a very active blog, this is one of their "Purely Educational" posts that we are reprinting here with their permission.
GPUs consist of multiple cores, each capable of executing thousands of mathematical operations simultaneously. This parallel architecture allows GPUs to tackle complex mathematical computations, such as matrix multiplications, Fourier transforms, and other linear algebra operations, much faster than CPUs.
GPUs are widely available on the market. From gaming to cryptomining to 3D modeling, consumers have no shortage of options to choose from. Unlike FPGAs, which often require specific configurations and can be challenging to program, many GPUs on the market come pre configured and ready to go. This user-friendly nature makes them accessible to a wide range of users and companies, ensuring they remain a popular choice in the tech industry.
FPGA advantages: Custom hardware acceleration
FPGAs are emerging as a powerful alternative to GPUs in the realm of artificial intelligence and high-performance computing. The biggest advantage of FPGAs is their programmability. Unlike GPUs which have a fixed design, FPGAs can be reprogrammed to implement custom logic and functionality. This programmability empowers developers to adapt the hardware to meet the specific requirements of their applications.
The ability to change the internal circuitry of FPGAs makes them an excellent choice for prototyping and development. Engineers can iterate quickly, testing different hardware configurations until they find the most efficient solution for their problem.
FPGAs often outshine GPUs in terms of latency and power usage, especially when fine-tuned for certain tasks. Developers can implement custom hardware accelerators tailored to specific tasks that may not be well-suited for the fixed architectures of GPUs. This allows FPGAs to offer a high degree of flexibility for fine-tuning hardware design to maximize efficiency. The caveat of course is graphics processing, where a high performance dedicated GPU will have better performance and power consumption.
Common use cases: FPGAs
Accelerating high-performance computing (HPCs)
Custom hardware acceleration means FPGAs are perfect for serving as programmable accelerators for inference in HPC clusters, which are great for training deep learning neural networks.
Real-time signal processing
FPGAs are very well-suited to applications that require low-latency and real-time signal processing, such as digital signal processing, radar systems, software-defined radios, and telecommunications.
Network optimization
FPGAs are ideal for offloading computationally intensive tasks, such as packet processing, encryption, and compression, from CPUs, reducing latency and increasing network throughput.
High-frequency trading
In trading, microseconds can be the difference between making millions of dollars and losing millions of dollars. High-frequency trading bots use FPGAs to implement custom algorithms that execute trades with minimum latency, providing a competitive advantage.
Aerospace and Defense applications
FPGAs are highly useful and beneficial in aerospace and defense systems, which use custom hardware accelerators for image and signal processing, encryption, and sensor data processing.
Common use cases: GPUs
In addition to gaming and rendering tasks, these are other typical GPU use cases:
Machine learning and deep learning
The popularity of artificial intelligence owes much to the exceptional processing power of GPUs. Training deep neural networks involves numerous matrix multiplications and activations, which GPUs handle with remarkable efficiency, significantly reducing training times.
Cryptocurrency mining
The mining of cryptocurrencies like Ethereum involves computationally intensive cryptographic operations, which GPUs can efficiently handle. For many years, GPU-based mining rigs dominated the scene, however, changes in mining difficulty, growing competition, and rising energy costs have reduced profit margins, so GPU mining is less popular than it used to.
Typical High-performance Computing applications
Scientific simulations, weather forecasting, and fluid dynamics simulations often require substantial computational power. GPUs provide the necessary horsepower to accelerate these simulations and improve time to results significantly.
Can You Use an FPGA as a GPU?
Yes, it's possible to use an FPGA as a GPU, but there are some important considerations and challenges involved. To use an FPGA as a GPU, you would need to design and implement a hardware architecture that emulates or replicates the functionality of a GPU. This requires significant expertise in FPGA design, as well as an in-depth understanding of GPU architecture and parallel processing techniques.
Also, keep in mind that while FPGAs can be highly efficient for specific tasks, they may not match the raw compute power and performance of modern GPUs, especially for graphics-intensive applications. FPGAs can also be power-hungry, and the power consumption of an FPGA-based GPU solution may not be as favorable as using dedicated GPUs.
Choosing between GPUs and FPGAs is an important decision that depends on the nature of the application, performance requirements, power constraints, and budget considerations. GPUs offer broad applicability and cost-effectiveness, making them a popular choice for many high-performance computing tasks. On the other hand, FPGAs provide a highly customizable and power-efficient solution for specific applications that demand hardware acceleration and real-time processing.
