Opportunities and Challenges of Performing Vector Operations inside the DRAM

Abstract

In order to overcome the low memory bandwidth and the high energy costs associated with the data transfer between the processor and the main memory, proposals on near-data computing started to gain acceptance in systems ranging from embedded architectures to high performance computing. The main previous approaches propose application specific hardware or require a large amount of logic. Moreover, most proposals require algorithm changes and do not make use of the full parallelism available on the DRAM devices. These issues limits the adoption and the performance of near-data computing. In this paper, we propose to implement vector instructions directly inside the DRAM devices, which we call the Memory Vector Extensions (MVX). This balanced approach reduces data movement between the DRAM to the processor while requiring a low amount of hardware to achieve good performance. Comparing to current vector operations present on processors, our proposal enable performance gains of up to 97x and reduces the energy consumption by up to 70x of the full system.