Machine-learning-based circuit synthesis

Lior Rokach, Meir Kalech, Gregory Provan, Alexander Feldman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Multi-level logic synthesis is a problem of immense practical significance, and is a key to developing circuits that optimize a number of parameters, such as depth, energy dissipation, reliability, etc. The problem can be defined as the task of taking a collection of components from which one wants to synthesize a circuit that optimizes a particular objective function. This problem is computationally hard, and there are very few automated approaches for its solution. To solve this problem we propose an algorithm, called Circuit-Decomposition Engine (CDE), that is based on learning decision trees, and uses a greedy approach for function learning. We empirically demonstrate that CDE, when given a library of different component types, can learn the function of Disjunctive Normal Form (DNF) Boolean representations and synthesize circuit structure using the input library. We compare the structure of the synthesized circuits with that of well-known circuits using a range of circuit similarity metrics.

Original languageAmerican English
Title of host publicationIJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence
Number of pages7
StatePublished - 1 Dec 2013
Event23rd International Joint Conference on Artificial Intelligence, IJCAI 2013 - Beijing, China
Duration: 3 Aug 20139 Aug 2013

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence


Conference23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence


Dive into the research topics of 'Machine-learning-based circuit synthesis'. Together they form a unique fingerprint.

Cite this