Deep Reinforcement Learning for Optimization at Early Design Stages

被引：2

作者：

Servadei, Lorenzo ^{[1
,5
]}

Lee, Jin Hwa ^{[2
]}

Arjona Medina, Jose A. ^{[3
]}

Werner, Michael ^{[1
,2
]}

Hochreiter, Sepp ^{[3
]}

Ecker, Wolfgang ^{[1
,2
]}

Wille, Robert ^{[3
,4
]}

机构：

[1] Infineon Technol AG, D-85579 Neubiberg, Germany

[2] Tech Univ Munich, D-80333 Munich, Germany

[3] Johannes Kepler Univ Linz, A-4040 Linz, Austria

[4] Software Competence Ctr Hagenberg, A-4232 Hagenberg, Austria

[5] Infineon Technol AG, Adv Artificial Intelligence Unit, D-85579 Munich, Germany

来源：

IEEE DESIGN & TEST | 2023年 / 40卷 / 01期

关键词：

Optimization; Costs; Design methodology; User interfaces; Hardware; Reinforcement learning; Table lookup; Machine Learning; Design Automation; Reinforcement Learning; Combinatorial Optimization; Early Design Stages;

D O I：

10.1109/MDAT.2022.3145344

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Deep reinforcement learning is shown to improve the design cost of hardware char63software interfaces within an industrial design framework. Based on optimization preferences specified by a designer, the proposed approach generates optimized solutions.

引用

页码：43 / 51

页数：9

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