Thermal-Aware Design and Management for Search-based In-Memory Acceleration

Recently, Processing-In-Memory (PIM) techniques exploiting resistive RAM (ReRAM) have been used to accelerate various big data applications. ReRAM-based in-memory search is a powerful operation which efficiently finds required data in a large data set. However, such operations result in a large amount of current which may create serious thermal issues, especially in state-of-the-art 3D stacking chips. Therefore, designing PIM accelerators based on in-memory searches requires a careful consideration of temperature. In this work, we propose static and dynamic techniques to optimize the thermal behavior of PIM architectures running intensive in-memory search operations. Our experiments show the proposed design significantly reduces the peak chip temperature and dynamic management overhead. We test our proposed design in two important categories of applications which benefit from the search-based PIM acceleration - hyper-dimensional computing and database query. Validated experiments show that the proposed method can reduce the steady-state temperature by at least 15.3 °C which extends the lifetime of the ReRAM device by 57.2% on average. Furthermore, the proposed fine-grained dynamic thermal management provides 17.6% performance improvement over stateof-the-art methods.