ApproxPIM: Exploiting realistic 3D-stacked DRAM for energy-efficient processing in-memory

Yibin Tang, Ying Wang, Huawei Li, Xiaowei Li

Abstract: Processing-in-Memory (PIM), has recently been revisited as one of the most promising solutions to deal with the issue of bandwidth and power wall between processor and memory. In this paper, we propose a light-weight PIM architecture, approxPIM, which leverages approximate computing techniques to enable InMemory Processing in a realistic 3D-stacked DRAM, Micron's Hybrid Memory Cube (HMC). Using the newly-released atomic instruction support of the HMC, approxPIM can process a wide range of data-intensive applications without adding any logic resources into the memory devices. Furthermore, we propose to approximate those accuracy-insensitive applications with the limited functioning set of HMC commands so that they could be smoothly mapped to the HMCs without the inference from processors, therefore enabling energy-efficient Processing-in-Memory and greatly expanding the scope of target PIM applications with HMC. In general, approxPIM gives a comprehensive study on HMC's potential and weakness in the application of Processing-in-Memory. Evaluation results show that our approxPIM significantly boosts the energy-efficiency and performance of the whole system.

External IDs:dblp:conf/aspdac/TangWLL17