MediaBench: a tool for evaluating and synthesizing multimedia and communicatons systems
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Cache-Aware Scratchpad Allocation Algorithm
Proceedings of the conference on Design, automation and test in Europe - Volume 2
An integrated hardware/software approach for run-time scratchpad management
Proceedings of the 41st annual Design Automation Conference
Compilation techniques for energy reduction in horizontally partitioned cache architectures
Proceedings of the 2005 international conference on Compilers, architectures and synthesis for embedded systems
Dynamic data scratchpad memory management for a memory subsystem with an MMU
Proceedings of the 2007 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
A novel technique to use scratch-pad memory for stack management
Proceedings of the conference on Design, automation and test in Europe
Processor energy characterization for compiler-assisted software energy reduction
Journal of Electrical and Computer Engineering
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Energy consumption has always been considered as the key issue of the state-of-the-art SoCs. Implementing an on-chip Cache is one of the most promising solutions. However, traditional Cache may suffer from performance and energy penalties due to the Cache conflict. In order to deal with this problem, this paper firstly introduces a Time-Slotted Cache Conflict Graph to model the behavior of Data Cache conflict. Then, we implement an Integer Nonlinear Programming to select the most profitable data pages and employ Virtual Memory System to remap those data pages, which can cause severe Cache conflict within a time slot, to the on-chip Scratchpad Memory (SPM). In order to minimize the swapping overhead of dynamic SPM allocation, we introduce a novel SPM controller with a tightly coupled DMA to issue the swapping operations without CPU's intervention. The proposed method can optimize all of the data segments, including global data, heap and stack data in general, and reduce 24.83% energy consumption on average without any performance degradation.