Improving Energy Efficiency of Coarse-Grain Reconfigurable Arrays Through Modulo Schedule Compression/Decompression

Abstract

Modulo-scheduled course-grain reconfigurable array (CGRA) processors excel at exploiting loop-level parallelism at a high performance per watt ratio. The frequent reconfiguration of the array, however, causes between 25% and 45% of the consumed chip energy to be spent on the instruction memory and fetches therefrom. This article presents a hardware/software codesign methodology for such architectures that is able to reduce both the size required to store the modulo-scheduled loops and the energy consumed by the instruction decode logic. The hardware modifications improve the spatial organization of a CGRA’s execution plan by reorganizing the configuration memory into separate partitions based on a statistical analysis of code. A compiler technique optimizes the generated code in the temporal dimension by minimizing the number of signal changes. The optimizations achieve, on average, a reduction in code size of more than 63% and in energy consumed by the instruction decode logic by 70% for a wide variety of application domains. Decompression of the compressed loops can be performed in hardware with no additional latency, rendering the presented method ideal for low-power CGRAs running at high frequencies. The presented technique is orthogonal to dictionary-based compression schemes and can be combined to achieve a further reduction in code size.