An energy-efficient and scalable eDRAM-based register file architecture for GPGPU

Abstract

The heavily-threaded data processing demands of streaming multiprocessors (SM) in a GPGPU require a large register file (RF). The fast increasing size of the RF makes the area cost and power consumption unaffordable for traditional SRAM designs in the future technologies. In this paper, we propose to use embedded-DRAM (eDRAM) as an alternative in future GPGPUs. Compared with SRAM, eDRAM provides higher density and lower leakage power. However, the limited data retention time in eDRAM poses new challenges. Periodic refresh operations are needed to maintain data integrity. This is exacerbated with the scaling of eDRAM density, process variations and temperature. Unlike conventional CPUs which make use of multi-ported RF, most of the RFs in modern GPGPU are heavily banked but not multi-ported to reduce the hardware cost. This provides a unique opportunity to hide the refresh overhead. We propose two different eDRAM implementations based on 3T1D and 1T1C memory cells. To mitigate the impact of periodic refresh, we propose two novel refresh solutions using bank bubble and bank walk-through. Plus, for the 1T1C RF, we design an interleaved bank organization together with an intelligent warp scheduling strategy to reduce the impact of the destructive reads. The analysis shows that our schemes present better energy efficiency, scalability and variation tolerance than traditional SRAM-based designsPeer ReviewedPostprint (published version

Haws

“…In HAWS execution, when instructions retire, we use register renaming to avoid WARs and WAWs when storing results, as necessary. Registers files in the GPU are underutilized in many applications, which has been discussed in many recent studies [7,8,16]. Based on these studies and our evaluation, we propose to use the underutilized registers for register renaming.…”

A STT-RAM-based low-power hybrid register file for GPGPUs

“…The efficiency of using embedded DRAM (eDRAM) as the GPU register files has been investigated recently [13,14]. Compared with traditional SRAM, eDRAM provides higher density and lower leakage power, but it has limited data retention time.…”

A Survey of Methods for Analyzing and Improving GPU Energy Efficiency

“…(1) DVFS (dynamic voltage/frequency scaling) based techniques Jiao et al 2010;Ma et al 2012;Cebrian et al 2012;Lee et al 2011;Sheaffer et al 2005b;Chang et al 2008;Ren 2011;Anzt et al 2011;Ren et al 2012;Zhao et al 2012;Huo et al 2012;Keller and Gruber 2010;Abe et al 2012;Park et al 2006;Paul et al 2013] (2) CPU-GPU workload division based techniques [Takizawa et al 2008;Rofouei et al 2008;Ma et al 2012;Hamano et al 2009] and GPU workload consolidation (3) Architectural techniques for saving energy in specific GPU components, such as caches Lee et al 2011;Lashgar et al 2013;Arnau et al 2012;Rogers et al 2013;Lee and Kim 2012], global memory [Wang et al 2013;Rhu et al 2013], pixel shader [Pool et al 2011], vertex shader [Pool et al 2008], core data-path, registers, pipeline and thread-scheduling Chu et al 2011;Gebhart et al 2011;Jing et al 2013;Gilani et al 2012;Sethia et al 2013].…”