Temporal Partitioning and Scheduling Data Flow Graphs for Reconfigurable Computers
IEEE Transactions on Computers
System-on-a-chip processor synchronization support in hardware
Proceedings of the conference on Design, automation and test in Europe
A framework for reconfigurable computing: task scheduling and context management
IEEE Transactions on Very Large Scale Integration (VLSI) Systems - System Level Design
Hardware-Software Codesign for Dynamically Reconfigurable Architectures
FPL '99 Proceedings of the 9th International Workshop on Field-Programmable Logic and Applications
HW/SW codesign techniques for dynamically reconfigurable architectures
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A Complete Data Scheduler for Multi-Context Reconfigurable Architectures
Proceedings of the conference on Design, automation and test in Europe
ACM Transactions on Embedded Computing Systems (TECS)
Multitasking on reconfigurable architectures: microarchitecture support and dynamic scheduling
ACM Transactions on Embedded Computing Systems (TECS)
ASP-DAC '06 Proceedings of the 2006 Asia and South Pacific Design Automation Conference
Modern development methods and tools for embedded reconfigurable systems: A survey
Integration, the VLSI Journal
An enhanced leakage-aware scheduler for dynamically reconfigurable FPGAs
Proceedings of the 16th Asia and South Pacific Design Automation Conference
System-level power-performance tradeoffs for reconfigurable computing
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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In this paper, we explore the system-level power-performance trade-offs available when implementing streaming embedded applications on fine-grained reconfigurable architectures. We show that an efficient hardware-software partitioning algorithm is required when targeting low-power. However, if the application objective is performance, then we propose the use of dynamically reconfigurable architectures. This work presents a configuration-aware data size partitioning approach. We propose a design methodology that adapts the architecture and used algorithms to the application requirements. The methodology has been proven to work on a real research platform based on Xilinx devices. Finally, we have applied our methodology and algorithms to the case study of image sharpening, which is required nowadays in digital cameras and mobile phones.