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Coarse-Grained Reconfigurable Arrays (CGRAs) are a very promising platform, providing both, up to 10-100 MOps/mW of power efficiency and are software programmable. However, this cardinal promise of CGRAs critically hinges on the effectiveness of application mapping onto CGRA platforms. While previous solutions have greatly improved the computation speed, they have largely ignored the impact of the local memory architecture on the achievable power and performance. This paper motivates the need for memory-aware application mapping for CGRAs, and proposes an effective solution for application mapping that considers the effects of various memory architecture parameters including the number of banks, local memory size, and the communication bandwidth between the local memory and the external main memory.
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