Nowadays, the use of GPGPUs is growing in high-performance computing including embedded system. Demanding more processing power increase the size of Register File (RF) and Execution Unit (EU) in GPGPUs, that increase the power consumption. However, energy and power consumption are vital for the embedded system due to using a battery and a simple cooling system. In this paper, initially, we have proposed a simple method to identify duplicated data in RF of GPGPUs. Afterward, we propose a compression method to improve the energy efficiency of RF by eliminating duplicated data and consequently unallocating some of RF banks. Experimental results on standard benchmarks show that our compression method reduces the total RF power consumption by 15% on average by considering overhead degradation. Furthermore, we propose a computation reuse method in the EU to exploit computation redundancy. This method utilizes the compression information of RF to identify the identical computations and turn off the processing cores that execute them. Moreover, our computation reuse method improves the EU energy efficiency by 28.8% on average.

如今，GPGPU在包括嵌入式系统在内的高性能计算中的使用正在增长。要求更高的处理能力会增加GPGPU中寄存器文件（RF）和执行单元（EU）的大小，从而增加功耗。但是，由于使用电池和简单的冷却系统，能源和功耗对于嵌入式系统至关重要。在本文中，最初，我们提出了一种简单的方法来识别GPGPU射频中的重复数据。之后，我们提出一种压缩方法，通过消除重复数据并因此取消分配一些RF库来提高RF的能量效率。标准基准上的实验结果表明，考虑到开销降低，我们的压缩方法平均将总RF功耗降低了15％。此外，我们在欧盟提出了一种计算重用方法来利用计算冗余。该方法利用RF的压缩信息来识别相同的计算并关闭执行它们的处理核心。此外，我们的计算重用方法将欧盟的能源效率平均提高了28.8％。