FPGA Vs GPU for Deep Learning

 Deep Learning: GPU Vs FPGA

Most of the foundation of artificial intelligence (AI) Deep learning is a type of machine learning that uses multi-layered neural networks to simulate the complex decision-making abilities of the human brain. Beyond artificial intelligence (AI), deep learning supports a myriad of applications that improve automation in everyday goods and services, such as digital assistants, credit card fraud detection, voice-activated consumer electronics, and more. Mostly used for applications like speech recognition, image processing, and complicated decision-making, it can "read" and analyse enormous amounts of data to effectively perform complex computations.


Deep learning requires a vast amount of computing power. Since high-performance GPUs can handle large volumes of data across multiple cores and have enormous RAM capacities, they are frequently the best choice. Nevertheless, keeping many GPUs on-site can be highly costly to maintain and put a heavy burden on internal resources. On the other hand, field programmable gate arrays (FPGAs), a flexible but potentially pricey solution, offer reprogrammable flexibility for new applications and respectable performance.

GPU vs FPGA

The choice of hardware has a significant influence on the scalability, performance, and efficiency of deep learning applications. When selecting a GPU or FPGA for a deep learning system, take goals, budget, and operational requirements into account. GPUs and FPGAs provide excellent CPU circuitry. Several NVIDIA and Xilinx GPU and FPGA solutions are optimised for the most recent PCIe standards.


When comparing hardware design frameworks, it is imperative to consider the following factors:

Performance velocities
Energy consumption
Scale economy
The capacity to programme

GPUs (graphics processing units):

GPUs are a type of specialised circuit made to operate memory rapidly, which expedites the creation of images. They perform especially well for parallel processing tasks like large-scale training of deep learning systems because to their high throughput design. Even while GPUs are frequently used in demanding applications like gaming and video processing, they are a wonderful option for intensive computations like processing enormous datasets, complex algorithms, and cryptocurrency mining because of their high-speed performance capabilities.

Because of their ability to carry out millions of simultaneous operations which are necessary for neural network inference and training GPUs are used in the artificial intelligence area.

Key features of GPUs

High-performance: Robust GPUs can easily handle applications like deep learning and high performance computing (HPC).
Parallel processing: GPUs excel at tasks that can be broken down into smaller components and finished concurrently.
GPUs are very powerful machines, but in exchange for their incredible processing capability, they have a high power consumption and low energy efficiency. For certain tasks like image processing, signal processing, or other AI applications, cloud-based GPU providers might provide a more cost-effective option through subscription or pay-as-you-go pricing methods.

GPU advantages

High computational power: GPUs provide the advanced floating-point computations needed for deep learning model training, which call for a high level of computing capacity.
High speed: By utilising their several internal cores to accelerate parallel processing, GPUs are able to perform multiple concurrent jobs with efficiency. Large datasets may be quickly evaluated by GPUs, which accelerates the training of machine learning models.
Strong development environments like CUDA and OpenCL, as well as manufacturers like Xilinx and Intel, are advantageous to GPUs.

GPU problems

Power consumption: The high power requirements of GPUs have an impact on the environment and increase operating expenses.
Less flexible: GPUs are much less flexible than FPGAs, with fewer possibilities for customising or application-specific optimisation.
FPGA expertise FPGAs are programmable silicon devices that may be adjusted and changed to suit different needs. FPGAs are more flexible than application-specific integrated circuits (ASICs), particularly in low-latency, specialised applications. FPGAs are highly valued in deep learning applications due to their adaptability, low power consumption, and agility.

While FPGAs may be reconfigured to optimise for specific applications, general-purpose GPUs cannot be reprogrammed, resulting in reduced power and delay. This important difference makes FPGAs particularly useful for real-time processing in AI applications and project development.

Key features of FPGA-based programmable hardware: Configuring FPGAs is made easy by FPGA-based hardware description languages (HDL), like Verilog or VHDL.
Energy Efficiency: FPGAs use less power than traditional processors, which reduces running costs and benefits the environment.
Even though they may not be as powerful as other processors, FPGAs are typically more efficient. For deep learning applications, such as handling large datasets, GPUs are recommended. On the other hand, reprogrammable cores on the FPGA offer tailored optimizations that can be more suitable for specific workloads and applications.

FPGA advantages

Personalisation: Programmability, a crucial aspect of FPGA design, makes prototyping and fine-tuning easier and is beneficial in the quickly expanding deep learning industry.
Low latency: Because FPGAs are reprogrammable, it is easier to tailor them for real-time applications.
FPGA issues
Low power: Although FPGAs are valued for their energy efficiency, their low power makes them less effective in more taxing tasks.
labor-intensive Although programmability is the main feature that attracts customers to FPGA chips, programmability is not only supported by FPGAs but is also essential to them. FPGA programming and reprogramming may cause delays in deployments.

GPU vs FPGA For in-depth education

Building deep neural networks (DNNs), or neural networks with three or more layers, is the definition of deep learning applications. Neural networks create decisions by recognising events, weighing options, and drawing conclusions using processes akin to those of biological neurons.

To learn to detect phenomena, recognise patterns, evaluate options, and make predictions and judgements, a DNN must be trained on a large amount of data. Furthermore, a lot of computer power is needed to process this data. GPUs and FPGAs are two options for supplying this power, but each has pros and cons.

FPGAs are effective in low-latency specialist systems that require customisation for specific deep learning needs, such as unique AI applications. FPGAs are also well suited for tasks where energy efficiency takes precedence over processing performance.

However, more complex tasks like training and running large models usually demand for GPUs with more power. Larger datasets can be handled more effectively using the GPU due to its enhanced processing power.

Applications for FPGAs

FPGAs are widely used in the following applications because of their low latency, power efficiency, and flexibility in programmability:

Low-latency real-time signal processing is necessary for telecommunications, driverless cars, radar systems, and digital signal processing.
Edge computing: By bringing compute and storage closer to the user, edge computing makes use of FPGAs, which are small and use less power.
Customised hardware acceleration: Configurable FPGAs can be tuned to accelerate certain deep learning jobs and HPC clusters by optimising for specific data types or methodologies.

GPU-based programmes

Applications that demand more processing power and preprogrammed features, like the following, profit immensely from general purpose GPUs:

High-performance computing: Because GPUs require a lot of processing power, they are crucial for companies like data centres and research institutes that manage large databases, do complex computations, or run simulations.
Large-scale models: Because GPUs are specifically made for fast parallel processing and have a great capacity for simultaneous matrix multiplications, they are often utilised to reduce the training times for large-scale deep learning models.
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When evaluating FPGA vs GPU, consider the potential of cloud infrastructure for your deep learning applications. With IBM GPU on Cloud, you can use NVIDIA GPUs for visualisation, classical AI, HPC, and generative AI use cases on dependable, reasonably priced, and secure IBM Cloud infrastructure. Increase your HPC and AI development by leveraging IBM's scalable enterprise cloud.

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