Instruction Set Design Methodology for In-Memory Computing through QEMU-based System Emulator

Abstract

In-Memory Computing (IMC) is a promising paradigm to mitigate the von Neumann bottleneck. However its evaluation on complete applications in the context of full-scale systems is limited by the complexity of simulation frameworks as well is the disjunction between hardware exploration and compiler support. This paper proposes a global exploration flow in the scale of Instruction Set Architectures (ISA) to perform both modeling and the generation of compiler support to perform ISA-level exploration. Our emulation methodology is based on QEMU, implements a performance model based on hardware characterizations from the State-of-the-Art, and allows the modeling of cache hierarchies, while our compiler support is automatically generated and based on a specialized compiler. We evaluate three applications in the domains of image processing and linear algebra on a reference IMC architecture, and analyze the obtained results to validate our methodology.