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Field-programmable gate arrays (FPGAs) have been around for a few decades now. These devices have an array of logic blocks and a way to program the blocks and the relationships among them. An FPGA is a generic tool that can be customized for multiple uses. In contrast to classic chips, FPGAs can be reconfigured multiple times for different purposes. To specify the configuration of an FPGA, developers use hardware description languages (HDLs) such as Verilog and VHDL. Modern FPGAs work pretty similarly to application-specific integrated circuits (ASICs): when re-programmed properly, FPGAs can match the requirements of a particular application just as a regular ASIC. And when it comes to data processing acceleration, FPGAs can outperform graphics processing units (GPUs).
There is a wide range of FPGA applications. You can configure an FPGA with thousands of memory units. This enables the circuits to work in a massively-parallel computing model, like GPUs. With FPGAs, you gain access to an adaptable architecture that enables you to optimize throughput. This means you can use FPGAs to meet or exceed the performance of GPUs.
Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. The general idea of Artificial Intelligence, with machines possessing the intelligence of humans, has been around since at least the middle of the 20th century. Machine learning blossomed much later, starting in the 1980s. Machine learning is based on parsing data, learning from it, and using that knowledge to make certain predictions or “train” the machine to perform specific tasks. For learning, machine learning systems use different algorithms: clustering, decision tree, Bayesian networks, and so on. Deep learning is an approach to machine learning. An important part of it is neural networks, resource-hungry and complex learning models that were nearly impossible to use for real-world tasks before the first GPUs and parallel task processing was invented.
In a nutshell, FPGAs deserve a place among GPU and CPU-based AI chips for big data and machine learning. They show great potential for accelerating AI-related workloads, inferencing in particular. The main advantages of using an FPGA for accelerating machine learning and deep learning processes are their flexibility, custom parallelism, and the ability to be reprogrammed for multiple purposes.
Department of Electronic Engineering, QUEST, Nawabshah is among one of the best departments of the University. We have a team of great and dedicated professionals and experts (PhDs) in all the fields derived from electronics.
Dr Abdul Rafay Khatri
Digital System Design Group
Department of Electronic Engineering,
QUEST Nawabshah.