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Reconfigurable arrays combine the benefit of spatial execution, typical of hardware solutions, with that of programmability, present in microprocessors. When mapping software applications (or parts of them) onto hardware, however, fine-grain arrays, such as field-programmable gate arrays (FPGAs), often provide more flexibility than is needed, and do not implement coarser-level operations efficiently. Therefore, coarse grained reconfigurable arrays (CGRAs) have been proposed to this aim. Most CGRA design emerged in research present ad-hoc solutions in many aspects; in this paper we propose an architectural template to enable design space exploration of different possible CGRA designs. We called the template expression-grained reconfigurable array (EGRA), as its ability to generate complex computational cells, executing expressions as opposed to single operations, is a defining feature. Other notable EGRA characteristics include the ability to support heterogeneous cells and different storage requirements through various memory interfaces. The performed design explorations, as shown trough the experimental data provided, can effectively drive designers to further close the performance gap between reconfigurable and hardwired logic by providing guidelines on architectural design choices. Performance results on a number of embedded applications show that EGRA instances can be used as a reconfigurable fabric for customizable processors, outperforming more traditional CGRA designs.Index Terms-Coarse grained reconfigurable architectures (CGRAs), design space exploration.

Embedded application requirements, including high performance, low power consumption and fast time to market, are uncommon in the broader domain of general purpose applications. In order to satisfy these demands, chip manufacturers often provide developers with the possibility to define application-specific Instruction Set Extensions (ISEs). Many techniques have been proposed that automatically identify the most beneficial ISEs from source code, so that compilers can identify the 'best' instruction set for the underlying machine. However, can we simply retrofit these techniques into a traditional compiler, or does ISE identification demand different tuning of the heuristics utilized throughout the optimization pipeline? In this paper, we show why compilers should sometimes make different decisions when targeting customized processors, and we show how traditional ISE identification techniques can improve significantly if the code is properly transformed in order to expose more beneficial extensions. The proposed approach was validated using the SimpleScalar simulator for the ARM processor, augmented with the possibility to define additional instructions. Using benchmarks taken from the MiBench suite, we show that the proposed transformations improve state of the art ISE identification techniques by 55% on average and 4x maximum.

Abstract-Reconfigurable Arrays combine the benefit of spatial execution, typical of hardware solutions, with that of programmability, present in microprocessors. When mapping software applications (or parts of them) onto hardware, however, FPGAs often provide more flexibility than is needed, and do not implement coarser-level operations efficiently. Therefore, Coarse Grained Reconfigurable Arrays (CGRAs) have been proposed to this aim. While most CGRA designs feature an array cell of the order of an ALU, this paper proposes a new kind of coarse grained array, called EGRA (Expression-Grained Reconfigurable Array), featuring a cell composed of a cluster of ALUs with flexible interconnect. The EGRA attempts to further close the performance gap between reconfigurable and hardwired logic by implementing an arithmetic/logic expression per cell, rather than a single operation. A mapping methodology is proposed that can retargetably compile to a family of EGRAs, therefore enabling architectural exploration of the granularity of the proposed cell. Performance results on a number of embedded applications show that EGRAs can be used as a reconfigurable fabric for customizable processors, outperforming more traditional CGRA designs.

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