Learning to Compare Hardware Designs for High-Level Synthesis

• URL: http://arxiv.org/abs/2409.13138v1
• Date: Fri, 20 Sep 2024 00:47:29 GMT
• Title: Learning to Compare Hardware Designs for High-Level Synthesis
• Authors: Yunsheng Bai, Atefeh Sohrabizadeh, Zijian Ding, Rongjian Liang, Weikai Li, Ding Wang, Haoxing Ren, Yizhou Sun, Jason Cong,
• Abstract summary: High-level synthesis (HLS) is an automated design process that transforms high-level code into hardware designs. HLS relies on pragmas, which are directives inserted into the source code to guide the synthesis process. We propose compareXplore, a novel approach that learns to compare hardware designs for effective HLS optimization.
• Score: 44.408523725466374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High-level synthesis (HLS) is an automated design process that transforms high-level code into hardware designs, enabling the rapid development of hardware accelerators. HLS relies on pragmas, which are directives inserted into the source code to guide the synthesis process, and pragmas have various settings and values that significantly impact the resulting hardware design. State-of-the-art ML-based HLS methods, such as HARP, first train a deep learning model, typically based on graph neural networks (GNNs) applied to graph-based representations of the source code and pragmas. They then perform design space exploration (DSE) to explore the pragma design space, rank candidate designs using the model, and return the top designs. However, traditional DSE methods face challenges due to the highly nonlinear relationship between pragma settings and performance metrics, along with complex interactions between pragmas that affect performance in non-obvious ways. To address these challenges, we propose compareXplore, a novel approach that learns to compare hardware designs for effective HLS optimization. CompareXplore introduces a hybrid loss function that combines pairwise preference learning with pointwise performance prediction, enabling the model to capture both relative preferences and absolute performance. Moreover, we introduce a novel node difference attention module that focuses on the most informative differences between designs, enabling the model to identify critical pragmas impacting performance. CompareXplore adopts a two-stage DSE, where a pointwise prediction model is used for the initial design pruning, followed by a pairwise comparison stage for precise performance verification. In extensive experiments, compareXplore achieves significant improvements in ranking metrics and generates high-quality HLS results for the selected designs, outperforming the existing SOTA method.

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