Speaker
Description
In the context of scientific computing there is a growing interest
towards DNNs (Deep Neural Networks), which are being used in several
applications, spanning from medical images segmentation and
classification, to the on-line analysis of experimental data.
In addition to GPUs, FPGAs are also emerging as compute accelerators
promising higher energy-efficiency and lower latency, for the inference
phase of such DNNs.
In this talk, we introduce a 2D UNet medical image segmentation
application as an use case; then we focus on the implementation of
its inference phase on the FPGA; and finally we compare its
performance on an AMD-Xilinx Alveo U250 FPGA, with its performance on
Intel CPUs and NVIDIA GPU accelerators, in terms of: accuracy,
time-to-solution and energy-efficiency.
