FPGA-based Infrastructure, With RISC-V Prototype, to Enable Implementation & Evaluation of Cross-Layer Techniques in Real HW (Best Paper Award)

A technical paper titled “MetaSys: A Practical Open-Source Metadata Management System to Implement and Evaluate Cross-Layer Optimizations” was published by researchers at University of Toronto, ETH Zurich, and Carnegie Mellon University. This paper won the Best Paper Award at the HiPEAC 2023 conference.

Abstract:
“This paper introduces the first open-source FPGA-based infrastructure, MetaSys, with a prototype in a RISC-V core, to enable the rapid implementation and evaluation of a wide range of cross-layer techniques in real hardware. Hardware-software cooperative techniques are powerful approaches to improve the performance, quality of service, and security of general-purpose processors. They are however typically challenging to rapidly implement and evaluate in real hardware as they require full-stack changes to the hardware, OS, system software, and instruction-set architecture (ISA).

MetaSys implements a rich hardware-software interface and lightweight metadata support that can be used as a common basis to rapidly implement and evaluate new cross-layer techniques. We demonstrate MetaSys’s versatility and ease-of-use by implementing and evaluating three cross-layer techniques for: (i) prefetching for graph analytics; (ii) bounds checking in memory unsafe languages, and (iii) return address protection in stack frames; each technique only requiring ~100 lines of Chisel code over MetaSys.

Using MetaSys, we perform the first detailed experimental study to quantify the performance overheads of using a single metadata management system to enable multiple cross-layer optimizations in CPUs. We identify the key sources of bottlenecks and system inefficiency of a general metadata management system. We design MetaSys to minimize these inefficiencies and provide increased versatility compared to previously-proposed metadata systems. Using three use cases and a detailed characterization, we demonstrate that a common metadata management system can be used to efficiently support diverse cross-layer techniques in CPUs.”

Find the technical paper here, related slides here and Github here. This latest version published January 2023.

Authors: Nandita Vijaykumar, Ataberk Olgun, Konstantinos Kanellopoulos, Nisa Bostancı, Hasan Hassan, Mehrshad Lotfi, Phillip B. Gibbons, Onur Mutlu. arXiv:2105.08123v5.