Reconfigurable FPGA Algorithms for Advancing Big Data Processing

Abstract

Due to the exponential growth of data in our increasingly digital world, there 
are both huge opportunities and big challenges. Artificial intelligence is slow
ly seeping into our lives, but big data analytics are quickly rising up to meet 
this growing demand without the help of traditional computing architectures 
that are struggling with the volume, velocity and variety of the data. Big data 
is solved using Field Programmable Gate Arrays (FPGAs), a reconfigurable 
hardware that can be optimized for a given big data workload. The article 
takes a deeper look at how reconfigurable algorithms for FPGA based big 
data processing are changing analytics capabilities in industries across the 
board. Together, the convergence of big data and reconfigurable comput
ing represents a paradigm shift in the type of analysis we perform on large 
scale data. Research and engineers are developing novel algorithms using the 
f
 lexibility and parallelism of FPGAs to be able to adapt in real time to chang
ing data patterns and processing requirements. By adopting an adaptive 
approach, new performance levels are released and organizations can gain 
actionable insight from massive datasets at unprecedented speed and accu
racy. This dive into this emerging field will cover fundamental reconfigurable 
algorithm concepts, state of the art FPGA architectures for big data, and real 
applications in fields such as finance, healthcare, and scientific computing. 
Not only will we also be looking ahead to what future trends and directions 
that will continue to further revolutionise how we process and analyse the 
expanding digital universe.