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High-level synthesis (HLS) is an automated design process that transforms high-level code into optimized hardware designs, enabling rapid development of efficient hardware accelerators for various applications such as image processing, machine learning, and signal processing. To achieve optimal performance, HLS tools rely on pragmas, which are directives inserted into the source code to guide the synthesis process, and these pragmas can 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 its pragmas. They then perform design space exploration (DSE) to explore the pragma design space, rank candidate designs using the trained model, and return the top designs as the final 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 values. Moreover, we introduce a novel Node Difference Attention module that focuses on the most informative differences between designs, enhancing the model’s ability to identify critical pragmas impacting performance. compareXplore adopts a two-stage DSE approach, where a pointwise prediction model is used for the initial design pruning, followed by a pairwise comparison stage for precise performance verification. Experimental results demonstrate that compareXplore achieves significant improvements in ranking metrics and generates high quality HLS results for the selected designs, outperforming the existing state-of-the-art method.
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High-Level Synthesis of Irregular Applications: A Case Study on Influence Maximization
FPGAs are promising platforms for accelerating irregular applications due to their ability to implement highly specialized hardware designs for each kernel. However, the design and implementation of FPGA-accelerated kernels can take several months using hardware design languages. High Level Synthesis (HLS) tools provide fast, high quality results for regular applications, but lack the support to effectively accelerate more irregular, complex workloads. This work analyzes the challenges and benefits of using a commercial state-of-the-art HLS tool and its available optimizations to accelerate graph sampling. We evaluate the resulting designs and their effectiveness when deployed in a state-of-the-art heterogeneous framework that implements the Influence Maximization with Martingales (IMM) algorithm, a complex graph analytics algorithm. We discuss future opportunities for improvement in hardware, HLS tools, and hardware/software co-design methodology to better support complex irregular applications such as IMM.
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- Award ID(s):
- 1812727
- PAR ID:
- 10438458
- Date Published:
- Journal Name:
- CF '23: Proceedings of the 20th ACM International Conference on Computing Frontiers
- Page Range / eLocation ID:
- 12 to 22
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3587135.3592196
