Recent years have witnessed the growing popularity of domain-specific
accelerators (DSAs), such as Google's TPUs, for accelerating various
applications such as deep learning, search, autonomous driving, etc. To
facilitate DSA designs, high-level synthesis (HLS) is used, which allows a
developer to compile a high-level description in the form of software code in C
and C++ into a design in low-level hardware description languages (such as VHDL
or Verilog) and eventually synthesized into a DSA on an ASIC
(application-specific integrated circuit) or FPGA (field-programmable gate
arrays). However, existing HLS tools still require microarchitecture decisions,
expressed in terms of pragmas (such as directives for parallelization and
pipelining). To enable more people to design DSAs, it is desirable to automate
such decisions with the help of deep learning for predicting the quality of HLS
designs. This requires us a deeper understanding of the program, which is a
combination of original code and pragmas. Naturally, these programs can be
considered as sequence data, for which large language models (LLM) can help. In
addition, these programs can be compiled and converted into a control data flow
graph (CDFG), and the compiler also provides fine-grained alignment between the
code tokens and the CDFG nodes. However, existing works either fail to leverage
both modalities or combine the two in shallow or coarse ways. We propose ProgSG
allowing the source code sequence modality and the graph modalities to interact
with each other 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 on two benchmark
datasets show the superiority of ProgSG over baseline methods that either only
consider one modality or combine the two without utilizing the alignment
information.

该论文提出了一种名为 ProgSG 的方法，结合源代码序列模态和图模态，实现深入和精细的互动，以促进领域特定加速器（DSAs）的自动化设计并解决现有高级综合工具需要进行微体系结构决策的问题。