Abstract:
Because of the high precision that they offer, CNNs represent a very important model for
systems that do image identification. However, such a task has high costs. For this reason, the current goal is to implement designs that are fast, but at the same time not costly. GPUs are an alternative, but they do not offer the best solution due to their large power consumption. FPGAs on the other hand, suit more with CNNs systems because they consume less energy and have a flexible structure. The difficult part for FPGA
architectures is implementing CNN systems using HDL, which is not a platform on which
to program; it is simply hardware-level code to describe components of hardware like
registers and counters. With HLS, designers are now capable of using high-level languages like C or C++ to implement CNNs into FPGAs, because HLS “translates” or synthesizes the codes written in high-level languages into hardware-level code or RTL parameters. This thesis represents a review on the previous work done on the CNNs implementation on FPGAs using HLS and summarize the results obtained.
