FPGA vs. GPU: Which is Better for Deep Learning?

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multilayer neural networks to simulate the complex decision-making abilities of the human brain. In addition to artificial intelligence (AI), deep learning powers many applications that improve automation, including everyday products and services such as digital assistants, voice-enabled home appliances, credit card fraud detection, and more. Primarily used in tasks such as speech recognition, image processing, and complex decision-making, they can “read” and process large amounts of data to efficiently perform complex calculations.

Deep learning requires enormous amounts of computing power. In general, high-performance graphics processing units (GPUs) are ideal because they can handle large amounts of computation across multiple cores with lots of available memory. However, managing multiple GPUs on-premises can place large demands on internal resources and make scaling prohibitively expensive. Alternatively, field programmable gate arrays (FPGAs) offer a potentially expensive but versatile solution that provides both performance and reprogrammable flexibility suitable for new applications.

FPGAs and GPUs

Hardware choice has a significant impact on the efficiency, speed, and scalability of deep learning applications. While designing a deep learning system, it is important to consider your operational requirements, budget, and goals when choosing between GPUs and FPGAs. Considering the circuitry, both GPUs and FPGAs constitute an effective central processing unit (CPU) and offer a variety of options from manufacturers such as NVIDIA or Xilinx designed for compatibility with the latest Peripheral Component Interconnect Express (PCIe) standard.

Important considerations when comparing frameworks for hardware design include:

• performance speed
• power consumption
• cost effectiveness
• Programmability
• bandwidth

Understanding graphics processing units (GPUs)

GPUs are a type of specialized circuitry designed to quickly manipulate memory to speed up image creation. Built for high throughput, it is especially effective at parallel processing tasks, such as training large-scale deep learning applications. Although typically used for demanding applications such as gaming and video processing, GPUs’ high-speed performance capabilities make them an excellent choice for processing large data sets, complex algorithms, and compute-intensive applications such as cryptocurrency mining.

In the field of artificial intelligence, GPUs are chosen for their ability to perform thousands of concurrent tasks required for neural network training and inference.

Key Features of GPUs

• High performance: Powerful GPUs are adept at handling demanding computing tasks such as high-performance computing (HPC) and deep learning applications.
• parallel processing: GPUs excel at tasks that can be broken down into smaller tasks and processed simultaneously.

GPUs provide exceptional computing performance, but their impressive processing power comes at the cost of energy efficiency and high power consumption. For certain tasks, such as image processing, signal processing, or other AI applications, cloud-based GPU vendors may offer more cost-effective solutions through subscription or pay-as-you-go pricing models.

GPU Advantages

• High computational power: GPUs provide advanced processing power for complex floating point calculations required when training deep learning models.
• high speed: GPUs leverage multiple internal cores to speed up parallel operations and allow them to handle multiple concurrent tasks efficiently. GPUs can quickly process large data sets and significantly reduce the time it takes to train machine learning models.
• ecosystem Apply: GPUs benefit from support from leading manufacturers such as Xilinx and Intel, with a robust developer ecosystem and frameworks including CUDA and OpenCL.

GPU issue

• power consumption: GPUs require significant amounts of power to operate, which can increase operating costs and also contribute to environmental issues.
• Less flexible: GPUs are much less flexible than FPGAs and offer less opportunity for optimization or customization for specific tasks.

To learn more about GPUs, check out the following video:

Understanding Field Programmable Gate Arrays (FPGAs)

FPGAs are programmable silicon chips that can be configured (and reconfigured) for multiple applications. Unlike application-specific integrated circuits (ASICs), which are designed for specific purposes, FPGAs are known for their efficient flexibility, especially in custom, low-latency applications. In deep learning use cases, FPGAs are prized for their versatility, power efficiency, and adaptability.

While general-purpose GPUs are not reprogrammable, the reconfigurability of FPGAs allows for application-specific optimization, reducing latency and power consumption. These key differences make FPGAs particularly useful for real-time processing of AI applications and prototyping new projects.

Key features of FPGA

• programmable hardware: FPGAs can be easily configured using an FPGA-based hardware description language (HDL) such as Verilog or VHDL.
• power efficiency: FPGAs use less power than other processors, reducing operating costs and environmental impact.

FPGAs are not as powerful as other processors, but are generally more efficient. For deep learning applications, such as processing large data sets, GPUs are preferred. However, the FPGA’s reconfigurable cores allow for custom optimizations that may be better suited to specific applications and workloads.

Advantages of FPGA

• Custom: Programmability, a key part of FPGA design, enables fine-tuning and prototyping useful in the emerging field of deep learning.
• low latency: The reprogrammable nature of FPGAs makes them easier to optimize for real-time applications.

FPGA Challenge

• low power: FPGAs are prized for their energy efficiency, but their lower power makes them unsuitable for more demanding tasks.
• Labor intensive: Programmability is a major selling point for FPGA chips, but FPGAs not only provide programmability, they require it. FPGA programming and reprogramming can cause deployment delays.

FPGA vs GPU Comparison for Deep Learning Use Cases

Deep learning applications involve creating deep neural networks (DNNs), a type of neural network that by definition has at least three (but more) layers. Neural networks make decisions through a process that mimics the way biological neurons work together to identify phenomena, weigh options, and reach conclusions.

Before DNNs can learn how to identify phenomena, recognize patterns, evaluate probabilities, and make predictions and decisions, they must first be trained on large amounts of data. And processing this data requires large amounts of computing power. FPGAs and GPUs can deliver this performance, but each has advantages and disadvantages.

FPGAs are best suited for custom, low-latency applications that require customization for specific deep learning tasks, such as custom AI applications. FPGAs are also suitable for tasks that value energy efficiency over processing speed.

On the other hand, high-performance GPUs are generally preferred for heavy tasks such as training and running large, complex models. The superior processing power of GPUs makes them better suited to effectively manage large data sets.

FPGA use cases

Benefiting from versatile programmability, power efficiency, and low latency, FPGAs are frequently used for:

• real time process: Applications that require low-latency, real-time signal processing, such as digital signal processing, radar systems, autonomous vehicles, and communications.
• Edge Computing: Edge computing and its approach of moving compute and storage functions closer to end users locally benefit from the low power consumption and compact size of FPGAs.
• Custom hardware acceleration: Configurable FPGAs can be fine-tuned to accelerate specific deep learning workloads and HPC clusters by optimizing for specific data types or algorithms.

GPU use cases

General-purpose GPUs typically offer higher computational power and pre-programmed features, making them suitable for the following applications:

• High-Performance Computing: GPUs are an essential part of operations such as data centers or research facilities that use enormous computing power to run simulations, perform complex calculations, or manage large data sets.
• Large scale model: GPUs, designed for fast parallel processing, are especially capable of computing large numbers of matrix multiplications simultaneously and are often used to reduce training times for large deep learning models.

Take the next step

When comparing FPGAs and GPUs, consider the performance of cloud infrastructure for your deep learning projects. Cloud-based IBM GPUs allow you to provision NVIDIA GPUs for generative AI, traditional AI, HPC, and visualization use cases on the reliable, secure, and cost-effective IBM Cloud infrastructure. Accelerate your AI and HPC journey with IBM’s scalable enterprise cloud.

Explore GPUs on IBM Cloud