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  1. Free, publicly-accessible full text available December 13, 2025
  2. In recent years, domain-specific accelerators (DSAs) have gained popularity for applications such as deep learning and autonomous driving. To facilitate DSA designs, programmers use high-level synthesis (HLS) to compile a high-level description written in C/C++ into a design with low-level hardware description languages that eventually synthesize DSAs on circuits. However, creating a highquality HLS design still demands significant domain knowledge, particularly in microarchitecture decisions expressed as pragmas. Thus, it is desirable to automate such decisions with the help of machine learning for predicting the quality of HLS designs, requiring a deeper understanding of the program that consists of original code and pragmas. Naturally, these programs can be considered as sequence data. In addition, these programs can be compiled and converted into a control data flow graph (CDFG). But existing works either fail to leverage both modalities or combine the two in shallow or coarse ways. We propose ProgSG, a model that allows interaction between the source code sequence modality and the graph modality in a deep and fine-grained way. To alleviate the scarcity of labeled designs, a pre-training method is proposed based on a suite of compiler’s data flow analysis tasks. Experimental results show that ProgSG reduces the RMSE of design performance predictions by up to 22%, and identifies designs with an average of 1.10× and 1.26× (up to 8.17× and 13.31×) performance improvement in design space exploration (DSE) task compared to HARP and AutoDSE, respectively. 
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    Free, publicly-accessible full text available September 9, 2025
  3. Free, publicly-accessible full text available August 1, 2025
  4. High-level synthesis (HLS) aims to raise the abstraction layer in hardware design, enabling the design of domain-specific accelerators (DSAs) targeted for field- programmable gate arrays (FPGAs) using C/C++ instead of hardware description languages (HDLs). Compiler directives in the form of pragmas play a crucial role in modifying the microarchitecture within the HLS framework. However, the number of possible microarchitectures grows exponentially with the number of pragmas. Moreover, the evaluation of each candidate design using the HLS tool consumes significant time, ranging from minutes to hours, leading to a slow optimization process. To accelerate this process, machine learning models have been used to predict design quality in milliseconds. However, existing open-source datasets for training such models are limited in terms of design complexity and available optimizations. In this paper, we present HLSYN, a new benchmark that addresses these limitations. It contains more complex programs with a wider range of optimization pragmas, making it a comprehensive dataset for training and evaluating design quality prediction models. The HLSYN benchmark consists of 42 unique programs/kernels, each of which has many different pragma configurations, resulting in over 42,000 labeled designs. We conduct an extensive comparison of state-of-the-art baselines to assess their effectiveness in predicting design quality. As an ongoing project, we anticipate expanding the HLSYN benchmark in terms of both quantity and variety of programs to further support the development of this field. 
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  5. Answering open-domain questions requires world knowledge about in-context entities. As pre-trained Language Models (LMs) lack the power to store all required knowledge, external knowledge sources, such as knowledge graphs, are often used to augment LMs. In this work, we propose knOwledge REasOning empowered Language Model (OREOLM), which consists of a novel Knowledge Interaction Layer that can be flexibly plugged into existing Transformer-based LMs to interact with a differentiable Knowledge Graph Reasoning module collaboratively. In this way, LM guides KG to walk towards the desired answer, while the retrieved knowledge improves LM. By adopting OREOLM to RoBERTa and T5, we show significant performance gain, achieving state-of-art results in the Closed-Book setting. The performance enhancement is mainly from the KG reasoning’s capacity to infer missing relational facts. In addition, OREOLM provides reasoning paths as rationales to interpret the model’s decision. 
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  6. Answering complex First-Order Logical (FOL) queries on large-scale incomplete knowledge graphs (KGs) is an important yet challenging task. Recent advances embed logical queries and KG entities in the same space and conduct query answering via dense similarity search. However, most logical operators designed in previous studies do not satisfy the axiomatic system of classical logic, limiting their performance. Moreover, these logical operators are parameterized and thus require many complex FOL queries as training data, which are often arduous to collect or even inaccessible in most real-world KGs. We thus present FuzzQE, a fuzzy logic based logical query embedding framework for answering FOL queries over KGs. FuzzQE follows fuzzy logic to define logical operators in a principled and learning-free manner, where only entity and relation embeddings require learning. FuzzQE can further benefit from labeled complex logical queries for training. Extensive experiments on two benchmark datasets demonstrate that FuzzQE provides significantly better performance in answering FOL queries compared to state-of-the-art methods. In addition, FuzzQE trained with only KG link prediction can achieve comparable performance to those trained with extra complex query data. 
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