{"type":"ScholarlyArticle","title":"Machine-Learning-Based Circuit Synthesis","authors":[{"name":"Lior Rokach","slug":"lior-rokach","given":"Lior","family":"Rokach","affiliation":""},{"name":"Meir Kalech","slug":"meir-kalech","given":"Meir","family":"Kalech","affiliation":""},{"name":"Gregory M. Provan","slug":"gregory-m-provan","given":"Gregory M.","family":"Provan","affiliation":""},{"name":"Alexander Feldman","slug":"alexander-feldman","given":"Alexander","family":"Feldman","affiliation":""}],"abstract":"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.","doi":null,"url":"https://ijcai.org/proceedings/2013/243","pdf":"https://ijcai.org/proceedings/2013/0243.pdf","markdown":"https://ijcai.org/proceedings/2013/243.md","bibtex":"https://ijcai.org/proceedings/2013/bibtex/243","volume":{"slug":"2013","title":"Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2013","url":"https://ijcai.org/proceedings/2013/","year":2013,"location":"Beijing, China","dates":"3–9 August 2013","editors":"Francesca Rossi","isbn":"978-1-57735-633-2","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":243,"pages":{"first":1635,"last":1640},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}