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Massachusetts Institute of Technology

Accelerating RTL Simulation Through Fine-grained Task Dataflow and Selective Execution

Abstract

dc:description.abstract

Fast simulation of digital circuits is crucial to build modern chips. Current processors and SoCs integrate hundreds of complex components, including cores, accelerators, and memory hierarchies. Simulating these systems is necessary to verify correctness and explore the design space. Simulation can happen at different levels of abstraction. In this work we focus on Register-Transfer-Level (RTL) simulation. While RTL simulators are frequently used in development due to their quick compilation times, their runtime performance is slow. This is because as the designs are scaled up, multicore communication and scheduling overheads limit performance and scalability. We present ASH, a parallel architecture tailored to RTL simulation workloads. ASH consists of a tightly codesigned hardware architecture and compiler for RTL simulation. ASH exploits two key opportunities. First, it performs dataflow execution of small tasks to leverage the fine-grained parallelism in simulation workloads. Second, it performs selective event-driven execution to run only the fraction of the design exercised each cycle, skipping ineffectual tasks. ASH hardware provides a novel combination of dataflow and speculative execution, and ASH’s compiler features several novel techniques to automatically leverage this hardware. We evaluate ASH in simulation using large Verilog designs that represent different types of architectures. With 256 simple cores, ASH is gmean 1,485× faster than 1-core Verilator, and it is 32× faster than Verilator on a server CPU with 32 complex cores while using 3× less area.

Degree

thesis:*
Name thesis:degree_name
Master
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Elsabbagh, Fares
Advisors dc:contributor.advisor
  • Sanchez, Daniel
  • Emer, Joel

Rights

dc:rights
Statement dc:rights
  • In Copyright - Educational Use Permitted
  • Copyright retained by author(s)

Identifiers

dc:identifier.*
Handle dc:identifier.uri
https://hdl.handle.net/1721.1/164508
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/164508

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
related terms
citation

Elsabbagh, Fares. Accelerating RTL Simulation Through Fine-grained Task Dataflow and Selective Execution. Massachusetts Institute of Technology, 2023. https://hdl.handle.net/1721.1/164508