TY - GEN

T1 - Machine-learning-based circuit synthesis

AU - Rokach, Lior

AU - Kalech, Meir

AU - Provan, Gregory

AU - Feldman, Alexander

PY - 2013/12/1

Y1 - 2013/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84896062523&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84896062523

SN - 9781577356332

T3 - IJCAI International Joint Conference on Artificial Intelligence

SP - 1635

EP - 1641

BT - IJCAI 2013 - Proceedings of the 23rd International Joint Conference on Artificial Intelligence

T2 - 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013

Y2 - 3 August 2013 through 9 August 2013

ER -