Embedded processors rely on the efficient use of instruction-level parallelism to answer the performance and energy needs of modern applications. Though improving performance is the primary goal for processors in general, it might lead to a negative impact on energy consumption, a particularly critical constraint for current systems. In this paper, we present SoMMA, a software-managed memory architecture for embedded multi-issue processors that can reduce energy consumption and energy-delay product (EDP), while still providing an increase in memory bandwidth. We combine the use of software-managed memories (SMM) with the data cache, and leverage the lower energy access cost of SMMs to provide a processor with reduced energy consumption and EDP. SoMMA also provides a better overall performance, as memory accesses can be performed in parallel, with no cost in extra memory ports. Compiler-automated code transformations minimize the programmer's effort to benefit from the proposed architecture. The approach shows average speedups of 1.118x and 1.121x, while consuming up to 11% and 12.8% less energy when comparing two modified ρVEX processors and their baselines, at full-system level comparisons. SoMMA also shows reduction of up to 41.5% on full-system EDP, maintaining the same processor area as baseline processors.

嵌入式处理器依靠有效使用指令级并行机制来满足现代应用程序的性能和能源需求。尽管提高性能通常是处理器的主要目标，但它可能导致对能耗的负面影响，这对当前系统而言尤其重要。在本文中，我们介绍了SoMMA，这是一种用于嵌入式多问题处理器的软件管理的内存体系结构，可以降低能耗和能源延迟产品（EDP），同时仍提供增加的内存带宽。我们将软件管理存储器（SMM）的使用与数据缓存结合起来，并利用SMM较低的能源访问成本为处理器提供了降低的能耗和EDP。SoMMA还提供了更好的整体性能，因为可以并行执行内存访问，无需额外的内存端口。编译器自动化的代码转换可最大程度地减少程序员从建议的体系结构中受益的精力。该方法在全系统级别的比较中，将两个改进的ρVEX处理器及其基线进行比较时，平均速度提高了1.118倍和1.121倍，而能耗分别降低了11％和12.8％。SoMMA还显示出在全系统EDP上最多减少了41.5％，并保持与基准处理器相同的处理器面积。