In the previous articles, we talked about Hardware Acceleration with FPGAs, the Key concepts about acceleration with FPGA that they provide, and the Hardware acceleration applications with FPGAs. In this latest installment of the series, we will focus on Hardware Accelerated Libraries and Frameworks with FPGAs, which implies zero changes to the code of an application. We will review the different alternatives available, for Machine and Deep Learning applications, Image, and Video Processing, as well as Databases.
Options for development with FPGAs
Historically, working with FPGAs has always been associated with the need for a Hardware developer, mainly Electronic Engineers, and the use of tools and Hardware Description Languages (HDL), such as VHDL and Verilog (of the concurrent type in instead of sequential), very different from those used in the field of Software development. In recent years, a new type of application has appeared, acceleration in data centers, which aims to reduce the gap between the Hardware and Software domains, for the cases of computationally demanding algorithms, with processing of large volumes of data.