# LightningSim: Fast and Accurate Trace-Based Simulation for High-Level Synthesis

arXiv: https://arxiv.org/abs/2304.11219 · HTML: https://mlsanity.com/paper/2304.11219/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Apr 2023.

## Built on by (most cited first, 2 of 2)

- [HLSFactory: A Framework Empowering High-Level Synthesis Datasets for Machine Learning and Beyond](https://mlsanity.com/paper/2405.00820/) (2024)
- [INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order Gradient Computations in Implicit Neural Representation Processing](https://mlsanity.com/paper/2308.05930/) (2023)

## Abstract (arXiv)

High-Level Synthesis allows hardware designers to create complex RTL designs using C/C++. The traditional HLS workflow involves iterations of C/C++ simulation for partial functional verification and HLS synthesis for coarse timing estimates. However, neither C/C++ simulation nor HLS synthesis estimates can account for complex behaviors like FIFO interactions and pipeline stalls, thereby obscuring problems like deadlocks and latency overheads. Such problems are revealed only through C/RTL co-simulation, which is typically orders of magnitude slower than either C/C++ simulation or HLS synthesis, far too slow to integrate into the edit-run development cycle. Addressing this, we propose LightningSim, a fast simulation tool for HLS that combines the speed of native C/C++ with the accuracy of C/RTL co-simulation. LightningSim directly operates on the LLVM intermediate representation (IR) code and accurately simulates a hardware design's dynamic behavior. First, it traces LLVM IR execution to capture the run-time information; second, it maps the static HLS scheduling information to the trace to simulate the dynamic behavior; third, it calculates stalls and deadlocks from inter-function interactions to get precise cycle counts. Evaluated on 33 benchmarks, LightningSim produces 99.9%-accurate timing estimates up to 95x faster than RTL simulation. Our code is publicly available on GitHub.