Evolution of FGPA: What does the Future Hold?

Evolution of FGPA: What does the Future Hold?

In the coming times, FGPA is expected to expand further and find an increasing number of applications across modernizing industries. 

FREMONT, CA: There is no predetermined way in which technologies evolve. The initial ideas about how and where a particular technology will be used transforms with time. The same is true for FGPA. Conventionally, FPGAs were used as network function accelerators or network switches. Now, the functionality of FPGAs has grown to encompass new dimensions. The trend might stretch well into the future as FPGAs start facilitating advancements such as machine learning inference and high-performance computing. One can say with a certain degree of conviction that the next generation of FPGAs might be able to find their way into supercomputers.  

Companies in the FGPA domain are currently busy testing innovative products. In a few years from now, there might be diverse lines of FPGAs from every company. These diverse products will differ in performance and capacities, as well as prices. The architecture of the chips will change according to the functional requirements. However, industry leaders believe that machine learning-related capabilities will influence FGPA architecture the most. As industries make a beeline to transform into smart, technology-first entities, machine learning has captured everyone's attention. By enabling processing capabilities that facilitate machine learning, FPGAs are expected to gain unmatched momentum.

From machine learning training and inference at data centers to inference at the edge, FPGAs may offer the most promising interventions. The need for inference-related solutions will grow exponentially. Currently, the installed capacity for inference is quite limited, and thus, market reports suggest that companies have spent more on the training of neural networks. This trend is expected to reverse gradually and result in more spending on inference functions. Thus, going forward, the revenues that FGPA chips generate will involve the ability of inference in machine learning programs. A study of market reports also makes it apparent that machine learning inference at data centers and the edge will see double-digit growths.

The traditional FPGAs are going to give way to highly flexible and easily programmable FPGAs that will form the core of small and big devices and facilities. 

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