Deep Learning FPGA vs GPU
The majority of artificial intelligence’s (AI) foundation Deep learning is a kind of machine learning that mimics the intricate decision-making capabilities of the human brain by using multi-layered neural networks. Deep learning, which goes beyond artificial intelligence (AI), powers a plethora of applications that enhance automation in commonplace goods and services including voice-activated consumer electronics, digital assistants, credit card fraud detection, and more. Because of its ability to “read” and analyse vast amounts of data in order to efficiently carry out complex computations, it is mostly utilised for tasks like speech recognition, picture processing, and complex decision-making.
A significant amount of processing power is needed for deep learning. High-performance graphics processing units (GPUs) are often the best option because of their ability to process a lot of data across several cores and significant amounts of RAM. Nevertheless, it can be quite expensive to grow and place a significant strain on internal resources when maintaining several GPUs on-premises. On the other hand, reprogrammable flexibility for new applications and acceptable performance are provided by field programmable gate arrays (FPGAs), a flexible option that may also be expensive.
FPGA vs GPU
Deep learning applications’ scalability, performance, and efficiency are greatly impacted by the hardware selection. Consider operational needs, budget, and goals when choosing a GPU or FPGA for a deep learning system. FPGAs and GPUs are good CPU circuitry. Many GPU and FPGA solutions from NVIDIA and Xilinx are tuned for the latest PCIe standards.
The following factors are crucial to compare when comparing hardware design frameworks:
- Speeds of performance
- Power usage
- Economy of scale
- Ability to programme
- The bandwidth
Understanding of graphics processing units (GPUs):
GPUs are a kind of specialised circuit designed to operate memory quickly in order to speed up the process of creating images. Because of their high throughput design, they work particularly well for parallel processing activities like deep learning systems that need to be trained on a big scale. GPUs are a great option for heavy computations including processing massive datasets, complicated algorithms, and cryptocurrency mining because to their high-speed performance capabilities, even though they are often utilised in demanding applications like gaming and video processing.
GPUs are selected in the field of artificial intelligence due to their capacity to execute millions of simultaneous operations required for neural network inference and training.
Important characteristics of GPUs
- High-performance: Applications such as deep learning and high performance computing (HPC) can be handled with ease by powerful GPUs.
- Parallel processing: GPUs are particularly good at jobs that can be divided into smaller steps and completed in parallel.
Although GPUs are incredibly powerful devices, their high power consumption and low energy efficiency are a trade-off for their amazing processing capacity. Through subscription or pay-as-you-go pricing models, cloud-based GPU providers may offer a more affordable alternative for particular activities like image processing, signal processing, or other AI applications.
GPU benefits
- High computational power: When training deep learning models, sophisticated floating-point computations need for a high level of computing power, which GPUs supply.
- High speed: GPUs handle numerous concurrent tasks efficiently by using their several internal cores to speed up parallel operations. GPUs can swiftly evaluate large datasets, speeding up machine learning model training.
- GPUs benefit from manufacturers like Xilinx and Intel and powerful development environments like CUDA and OpenCL.
GPU issues
- Power consumption: GPUs need a lot of power to run, which can have an effect on the environment and raise operational costs.
- Less adaptable: Compared to FPGAs, GPUs are significantly less adaptable and offer fewer options for customisation or optimisation for particular applications.
FPGA knowledge
Programmable silicon devices called FPGAs can be tweaked and reconfigured to meet various demands. FPGAs are versatile, especially in low-latency, specialised applications, unlike application-specific integrated circuits (ASICs). FPGAs are prized for their agility, power economy, and versatility in deep learning applications.
General-purpose GPUs are not reprogrammable, but the reconfigurability of FPGAs enables optimisation for particular applications, resulting in lower power and delay. FPGAs are very helpful for real-time processing in AI applications and project prototyping because of this significant distinction.
Important characteristics of FPGAs
- Programmable hardware: FPGA-based hardware description languages (HDL), such Verilog or VHDL, make it simple to configure FPGAs.
- Power Efficiency: Compared to conventional processors, FPGAs consume less power, which lowers operating expenses and has a positive environmental impact.
FPGAs are usually more efficient than other processors, despite the fact that they might not be as powerful. GPUs are preferred for deep learning tasks, such as processing huge datasets. Reconfigurable cores on the FPGA, however, provide customised optimisations that can be more appropriate for particular workloads and applications.
FPGA benefits
- Customisation: A key component of FPGA design, programmability facilitates prototyping and fine-tuning, which is helpful in the rapidly developing deep learning sector.
- Low latency: FPGAs can be more easily optimised for real-time applications due to their reprogrammable nature.
FPGA difficulties
- Low power: FPGAs are appreciated for their energy efficiency, but they are not as good at more demanding jobs because of their low power.
- Labor-intensive Although the primary selling point of FPGA chips is their programmability, FPGAs don’t just support programmability they necessitate it. Deployments may be delayed by programming and reprogramming FPGAs.
FPGA vs GPU For deep learning
By definition, deep learning applications entail building deep neural networks (DNNs), which are neural networks with three or more layers. Neural networks use procedures that are similar to how biological neurons collaborate to recognise events, evaluate possibilities, and draw conclusions in order to make decisions.
A DNN needs to be trained on a lot of data before it can be taught to identify phenomena, recognise patterns, assess options, and make predictions and decisions. Furthermore, processing this data requires a lot of computer power. This power can be supplied by GPUs or FPGAs, but each has advantages and disadvantages.
FPGAs work well in low-latency specialised systems that need to be customised for certain deep learning requirements, like custom artificial intelligence applications. Tasks that prioritise energy efficiency over processing speed are also a good fit for FPGAs.
On the other hand, larger jobs like training and executing big, sophisticated models typically require GPUs with higher power. Because of its increased processing capability, the GPU is more suitable for handling bigger datasets efficiently.
FPGA applications
Due to its flexible programmability, low latency, and power efficiency, FPGAs are frequently utilised in the following applications:
- Digital signal processing, radar systems, driverless vehicles, and telecommunications require low-latency real-time signal processing.
- Edge computing: The FPGA’s tiny size and low power consumption make it suitable for edge computing, which brings compute and storage closer to the user.
- Customised hardware acceleration: By optimising for particular data types or methods, configurable FPGAs can be adjusted to accelerate particular deep learning tasks and HPC clusters.
GPU applications
Applications that benefit greatly from general purpose GPUs include those that require increased processing capability and preprogrammed capabilities, such as the following:
- High-performance computing: GPUs are essential to businesses like data centres and research institutes that manage big datasets, do complicated computations, or run simulations because they require a lot of processing power.
- Large-scale models: GPUs are typically used to shorten training durations for large-scale deep learning models since they are specifically designed for rapid parallel processing and can do a high number of matrix multiplications simultaneously.
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