Applications that benefit from hardware acceleration with FPGA chips

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In general, an application, which includes hard to learn algorithms with massive amount of data, where the processing time is long enough, is practically a right candidate for acceleration. It must be a process carried out through parallelization. Among the typical solutions for FPGA chips, which respond to these characteristics, we find several.

FPGA and machine learning

Machine Learning is one of the most prominent fields with significant advances in recent years. In this sense, hardware acceleration can be its greatest ally, thanks to the several advantages it brings, such as proper parallelism and vast number of matrix operations needed. These can be seen both in the training phase of the model, and in the inference phase, enabling real-time applications.

Image and video process acceleration

The image and video processing is one of the fields most benefited by FPGA acceleration, enabling real time work on tasks such as live streaming and image processing. Some of the applications are medical instruments for easy diagnostics, HD facial recognition, 3D augmented reality, modern military vehicles, etc.

FPGA and bulk data acceleration

The databases and analytical workloads are increasingly complex due to advances in ML, forcing an evolution of the data center. Hardware acceleration provides solutions to computing and storage. The large amount of data to be processed requires faster and more efficient storage systems. Obstruction is reduced thanks to the compression, encryption, and indexing.

FPGA and network acceleration

Almost same thing happens with network acceleration. FPGA adopts the concept of Logic Cell Array, which includes three parts: Configurable Logic Block, Input Output Block, and Interconnect. The high performance FPGA server computing is the practice of adding more computing power in such a way to deliver a superior performance to that of a conventional PC, to solve big problems in science and engineering.

Financial technology and FPGA

In the case of Financial Technology, time is the key to reducing risks, making informed business decisions and providing differentiated financial services. With FPGA, one can accelerate financial risk management process, negotiation process, evaluation process, etc. FPGA offers real time tools and services, assisting to automate designs. Every FPGA project is born to solve some problems encountered in reality, so every FPGA project has its own corresponding background knowledge.

They also serve to preserve hardware

As time passes, the hardware deteriorates. It is normal to replace it with a newer one with greater capacities and compatible with the existing one, but there are cases in which the company that designed the original hardware no longer exists and therefore the platform does not exist either. In this case, FPGA chips are helpful. You only need to reprogram the platform to support the hardware.

Distributed Vs. Heterogeneous Computing

In the last 10 years, we have witnessed an exponential growth in the generation of data, thanks to the emergence and popularity of electronic devices. However, it raises the question – how can we decrease execution times to make the proposed solutions more viable?

One of the solutions proposed is to use Distributed Computing. Another alternative is to use Heterogeneous Computing. FPGA-based accelerator cards have become an excellent complement for data centers, being available both on-premise and in cloud services.

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