Composing high-performance memory allocators
Proceedings of the ACM SIGPLAN 2001 conference on Programming language design and implementation
Dynamic Storage Allocation: A Survey and Critical Review
IWMM '95 Proceedings of the International Workshop on Memory Management
Proceedings of the conference on Design, automation and test in Europe - Volume 1
Energy-efficient dynamic memory allocators at the middleware level of embedded systems
EMSOFT '06 Proceedings of the 6th ACM & IEEE International conference on Embedded software
Proceedings of the conference on Design, automation and test in Europe
SCOPES '07 Proceedingsof the 10th international workshop on Software & compilers for embedded systems
SystemCoDesigner: automatic design space exploration and rapid prototyping from behavioral models
Proceedings of the 45th annual Design Automation Conference
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Design space exploration acceleration through operation clustering
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Divide and conquer high-level synthesis design space exploration
ACM Transactions on Design Automation of Electronic Systems (TODAES) - Special section on verification challenges in the concurrent world
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New applications in embedded systems are becoming increasingly dynamic. In addition to increased dynamism, they have massive data storage needs. Therefore, they rely heavily on dynamic, run-time memory allocation. The design and configuration of a dynamic memory allocation subsystem requires a big design effort, without always achieving the desired results. In this paper, we propose a fully automated exploration of dynamic memory allocation configurations. These configurations are fine tuned to the specific needs of applications with the use of a number of parameters. We assess the effectiveness of the proposed approach in two representative real-life case studies of the multimedia and wireless network domains and show up to 76% decrease in memory accesses and 66% decrease in memory footprint within the Pareto-optimal trade-off space.