Dynamic bandwidth scaling for embedded DSPs with 3D-stacked DRAM and wide I/Os

Authors:
Daniel W. Chang;Young Hoon Son;Jung Ho Ahn;Hoyoung Kim;Minwook Ahn;Michael J. Schulte;Nam Sung Kim
Affiliations:
University of Wisconsin - Madison, Madison, WI;Seoul National University, Seoul, South Korea;Seoul National University, Seoul, South Korea;Samsung Advanced Institute of Technology, Samsung Electronics Company, Yongin, Kyunggi, South Korea;Samsung Advanced Institute of Technology, Samsung Electronics Company, Yongin, Kyunggi, South Korea;University of Wisconsin - Madison, Madison, WI;University of Wisconsin - Madison, Madison, WI
Venue:
Proceedings of the International Conference on Computer-Aided Design
Year:
2013

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Abstract

3D main memory is an emerging technology that stacks DRAM dies underneath the processor die using through-silicon vias (TSVs). Prior studies assumed that such technology would decrease main memory access latency by 45% to 60%, while also allowing designers to increase main memory bandwidth. Although the latter is true, it was recently shown that the latency savings of 3D main memory is only 6.3%. In this paper, we first analyze memory latency reduction opportunities in a 3D main memory system with Wide I/O by taking better advantage of 3D integration technology and quantify their benefit. Specifically, redesigning the DRAM to memory controller synchronizers and placing the address, command, and data pads closer to the DRAM banks can decrease 3D main memory latency by 24.7%. We show that current 3D DRAM with Wide I/O can increase the geometric mean performance of an embedded processor that is similar to a Texas instrument C67x DSP by 9.7% (and up to 23.3%). Second, we observe that 3D DRAM with Wide IO can increase average system energy consumption of energy-constrained embedded DSPs by 2.6% (and up to 8.9%). To improve I/O energy efficiency, we propose to dynamically scale memory bandwidth (i.e. the I/O width) at runtime based on an application's program phases. Our dynamic bandwidth scaling algorithms increase average performance by 6.6% while increasing average energy consumption by only 0.5%.