Deep Neural Network Accelerated Implicit Filtering

TL;DR

This paper introduces DNNAIF, a novel method that combines implicit filtering with deep neural networks to accelerate derivative-free optimization, particularly useful for black-box problems like circuit design.

Contribution

The paper presents a new hybrid approach, DNNAIF, that enhances implicit filtering with neural network approximation for faster optimization in black-box scenarios.

Findings

Demonstrated effectiveness on the coverage directed generation problem

Accelerated convergence compared to traditional DFO methods

Applicable to complex simulation-based optimization tasks

Abstract

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.