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.

“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.

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