RTeAAL Sim: Using Tensor Algebra to Represent and Accelerate RTL Simulation

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Authors: Yan Zhu, Boru Chen, Christopher W. Fletcher, Nandeeka Nayak
Cited by: 1
References: 70

Abstract

RTL simulation on CPUs remains a persistent bottleneck in hardware design. State-of-the-art simulators embed the circuit directly into the simulation binary, resulting in long compilation times and execution that is fundamentally CPU frontend-bound, with severe instruction-cache pressure. This work proposes RTeAAL Sim, which reformulates RTL simulation as a sparse tensor algebra problem. By representing RTL circuits as tensors and simulation as a sparse tensor algebra kernel, RTeAAL Sim decouples simulation behavior from binary size and makes RTL simulation amenable to well-studied tensor algebra optimizations. We demonstrate that a prototype of our tensor-based simulator, even with a subset of these optimizations, already mitigates the compilation overhead and frontend pressure and achieves performance competitive with the highly optimized Verilator simulator across multiple CPUs and ISAs.

Fields of Study & Subjects

Tensor decomposition and applications Parallel Computing and Optimization Techniques Simulation Techniques and Applications

Publication details

Journal / source
Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2
Publisher
ACM
Pages
1660–1676
Publication date
10 March 2026