  In this paper, we illustrate a novel method for solving optimization problems
when derivatives are not explicitly available. We show that combining implicit
filtering (IF), an existing derivative free optimization (DFO) method, with a
deep neural network global approximator leads to an accelerated DFO method.
Derivative free optimization problems occur in a wide variety of applications,
including simulation based optimization and the optimization of stochastic
processes, and naturally arise when the objective function can be viewed as a
black box, such as a computer simulation. We highlight the practical value of
our method, which we call deep neural network accelerated implicit filtering
(DNNAIF), by demonstrating its ability to help solve the coverage directed
generation (CDG) problem. Solving the CDG problem is a key part of the design
and verification process for new electronic circuits, including the chips that
power modern servers and smartphones.
