FPGA vs. GPU: Which is better for deep learning?

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. Beyond artificial intelligence (AI), deep learning drives many applications that improve automation, including everyday products and services like digital assistants, voice-enabled consumer electronics, credit card fraud detection and more. It is primarily used for tasks like speech recognition, image processing and complex decision-making, where it can “read” and process a large amount of data to perform complex computations efficiently.

Deep learning requires a tremendous amount of computing power. Typically, high-performance graphics processing units (GPUs) are ideal because they can handle a large volume of calculations in multiple cores with copious memory available. However, managing multiple GPUs on-premises can create a large demand on internal resources and be incredibly costly to scale. Alternatively, field programmable gate arrays (FPGAs) offer a versatile solution that, while also potentially costly, provide both adequate performance as well as reprogrammable flexibility for emerging applications. 

FPGAs vs. GPUs

The choice of hardware significantly influences the efficiency, speed and scalability of deep learning applications. While designing a deep learning system, it is important to weigh operational demands, budgets and goals in choosing between a GPU and a FPGA. Considering circuitry, both GPUs and FPGAs make effective central processing units (CPUs), with many available options from manufacturers like NVIDIA or Xilinx designed for compatibility with modern Peripheral Component Interconnect Express (PCIe) standards.

When comparing frameworks for hardware design, critical considerations include the following:

• Performance speeds
• Power consumption
• Cost-efficiency
• Programmability
• Bandwidth

Understanding graphics processing units (GPUs)

GPUs are a type of specialized circuit that is designed to rapidly manipulate memory to accelerate the creation of images. Built for high throughput, they are especially effective for parallel processing tasks, such as training large-scale deep learning applications. Although typically used in demanding applications like gaming and video processing, high-speed performance capabilities make GPUs an excellent choice for intensive computations, such as processing large datasets, complex algorithms and cryptocurrency mining. 

In the field of artificial intelligence, GPUs are chosen for their ability to perform the thousands of simultaneous operations necessary for neural network training and inference. 

Key features of GPUs

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

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

GPU advantages

• High computational power: GPUs provide the high-end processing power necessary for the complex floating-point calculations that are required when training deep learning models. 
• High speed: GPUs make use of multiple internal cores to speed up parallel operations and enable the efficient processing of multiple concurrent operations. GPUs can rapidly process large datasets and greatly decrease time spent training machine learning models.
• Ecosystem support: GPU’s benefit from support by major manufacturers like Xilinx and Intel, with robust developer ecosystems and frameworks including CUDA and OpenCL.

GPU challenges

• Power consumption: GPUs require significant amounts of power to operate, which can increase operational expenses and also impact environmental concerns.
• Less flexible: GPUs are far less flexible than FPGAs, with less opportunity for optimizations or customization for specific tasks. 

For a deeper look into GPUs, check out the following video:

Understanding field programmable gate arrays (FPGAs)

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

While general-purpose GPUs cannot be reprogrammed, the FPGA’s reconfigurability allows for specific application optimization, leading to reduced latency and power consumption. This key difference makes FPGAs particularly useful for real-time processing in AI applications and prototyping new projects. 

Key features of FPGAs

• Programmable hardware: FPGAs can be easily configured with FPGA-based hardware description languages (HDL), such as Verilog or VHDL.
• Power Efficiency: FPGAs use less power compared to other processors, reducing operational costs and environmental impact. 

While FPGAs may not be as mighty as other processors, they are typically more efficient. For deep learning applications, such as processing large datasets, GPUs are favored. However, the FPGA’s reconfigurable cores allow for custom optimizations that may be better suited for specific applications and workloads.

FPGA advantages

• Customization: Central to FPGA design, programmability supports 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 challenges

• Low power: While FPGAs are valued for their energy efficiency, their low power makes them less suitable for more demanding tasks. 
• Labor intensive: While programmability is the FPGA chip’s main selling point, FPGAs don’t just offer programmability, they require it. FPGA programming and reprogramming can potentially delay deployments. 

FPGA vs. GPU for deep learning use cases

Deep learning applications, by definition, involve the creation of a deep neural network (DNN), a type of neural network with at least three (but likely many more) layers. Neural networks make decisions through processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.

Before a DNN can learn to identify phenomena, recognize patterns, evaluate possibilities and make predictions and decisions, they must be trained on large amounts of data. And processing this data takes a large amount of computing power. FPGAs and GPUs can provide this power, but each has their strengths and weaknesses.

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

Higher-powered GPUs, on the other hand, are generally preferred for heavier tasks like training and running large, complex models. The GPUs superior processing power makes it better suited for effectively managing larger datasets.    

FPGA use cases

Benefitting from versatile programmability, power efficiency and low latency, FPGAs are often used for the following:  

• Real-time processing: Applications requiring low-latency, real-time signal processing, such as digital signal processing, radar systems, autonomous vehicles and telecommunications.
• Edge computing: Edge computing and the practice of moving compute and storage capabilities closer locally to the end-user benefit from the FPGA’s low power consumption and compact size.
• Customized hardware acceleration: Configurable FPGAs can be fine-tuned to accelerate specific deep learning tasks and HPC clusters by optimizing for specific types of data types or algorithms. 

GPU use cases

General purpose GPUs typically offer higher computational power and preprogrammed functionality, making them bust-suited for the following applications: 

• High-performance computing: GPUs are an integral element of operations like data centers or research facilities that rely on massive computational power to run simulations, perform complex calculations or manage large datasets. 
• Large-scale models: Designed for speedy parallel processing, GPUs are especially capable at calculating a large number of matrix multiplications concurrently and are often used to expedite training times for large-scale deep learning models.

Take the next step

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

Explore GPUs on IBM Cloud