  Estimating Worst-Case Execution Time (WCET) is of utmost importance for
developing Cyber-Physical and Safety-Critical Systems. The system's scheduler
uses the estimated WCET to schedule each task of these systems, and failure may
lead to catastrophic events. It is thus imperative to build provably reliable
systems. WCET is available to us in the last stage of systems development when
the hardware is available and the application code is compiled on it. Different
methodologies measure the WCET, but none of them give early insights on WCET,
which is crucial for system development. If the system designers overestimate
WCET in the early stage, then it would lead to the overqualified system, which
will increase the cost of the final product, and if they underestimate WCET in
the early stage, then it would lead to financial loss as the system would not
perform as expected. This paper estimates early WCET using Deep Neural Networks
as an approximate predictor model for hardware architecture and compiler. This
model predicts the WCET based on the source code without compiling and running
on the hardware architecture. Our WCET prediction model is created using the
Pytorch framework. The resulting WCET is too erroneous to be used as an upper
bound on the WCET. However, getting these results in the early stages of system
development is an essential prerequisite for the system's dimensioning and
configuration of the hardware setup.
