Thermal-aware processing-in-memory instruction offloading

Abstract

With the advent of die stacking technology and big data applications, Processing-in-memory (PIM) is regaining attention as a promising technology for improving performance and energy efficiency. Although various PIM techniques have been proposed in recent studies for effectively offloading computation from the host, the thermal impacts of PIM offloading have not been fully explored. This paper investigates the thermal constraints of PIM and proposes techniques to enable thermal awareness for efficient PIM offloading. To understand the thermal effects of 3D-stacked designs, we measure the temperature of a real Hybrid Memory Cube (HMC) prototype and observe that compared to conventional DRAM, HMC reaches a significantly higher operating temperature, which causes thermal shutdowns with a passive cooling solution. Even with a commodity-server cooling solution, when in-memory processing is highly utilized, HMC fails to maintain the temperature of the memory dies within the normal operating range. In this paper, we propose a collection of software- and hardware-based techniques to enable thermal-aware PIM offloading by controlling the intensity of PIM offloading at runtime. Our evaluation results show that the proposed techniques achieve up to 1.4 × and 1.37× speedups compared to non-offloading and naïve offloading scenarios.