# ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation

arXiv: https://arxiv.org/abs/2107.11673 · HTML: https://mlsanity.com/paper/2107.11673/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2021.

## Built on by (most cited first, 21 of 21)

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- [SyntheticHLS: Building Diverse Synthetic High-Level Synthesis Datasets using LLMs](https://mlsanity.com/paper/2610.00106/) (2026)

## Abstract (arXiv)

High-level synthesis (HLS) has been widely adopted as it significantly improves the hardware design productivity and enables efficient design space exploration (DSE). Existing HLS tools are built using compiler infrastructures largely based on a single-level abstraction, such as LLVM. However, as HLS designs typically come with intrinsic structural or functional hierarchies, different HLS optimization problems are often better solved with different levels of abstractions. This paper proposes ScaleHLS, a new scalable and customizable HLS framework, on top of a multi-level compiler infrastructure called MLIR. ScaleHLS represents HLS designs at multiple representation levels and provides an HLS-dedicated analysis and transform library to solve the optimization problems at the suitable levels. Using this library, we provide a DSE engine to generate optimized HLS designs automatically. In addition, we develop an HLS C front-end and a C/C++ emission back-end to translate HLS designs into/from MLIR for enabling an end-to-end compilation flow. Experimental results show that, comparing to the baseline designs without manual directives insertion and code-rewriting, that are only optimized by Xilinx Vivado HLS, ScaleHLS improves the performances with amazing quality-of-results -- up to 768.1x better on computation kernel level programs and up to 3825.0x better on neural network models.