  The advent of modern cloud services along with the huge volume of data
produced on a daily basis, have set the demand for fast and efficient data
processing. This demand is common among numerous application domains, such as
deep learning, data mining, and computer vision. Prior research has focused on
employing hardware accelerators as a means to overcome this inefficiency. This
trend has driven software development to target heterogeneous execution, and
several modern computing systems have incorporated a mixture of diverse
computing components, including GPUs and FPGAs. However, the specialization of
the applications' code for heterogeneous execution is not a trivial task, as it
requires developers to have hardware expertise in order to obtain high
performance. The vast majority of the existing deep learning frameworks that
support heterogeneous acceleration, rely on the implementation of wrapper calls
from a high-level programming language to a low-level accelerator backend, such
as OpenCL, CUDA or HLS.
  In this paper we have employed TornadoVM, a state-of-the-art heterogeneous
programming framework to transparently accelerate Deep Netts; a Java-based deep
learning framework. Our initial results demonstrate up to 8x performance
speedup when executing the back propagation process of the network's training
on AMD GPUs against the sequential execution of the original Deep Netts
framework.
