arXiv subjects

Weiman Yan

Publications and source records attributed to Weiman Yan.

3 recordsLinked to original sources

arXiv2026

Pattern-Guided Design Space Exploration for FPGA Accelerator Design

High-level synthesis (HLS) raises the abstraction level of FPGA accelerator design from hardware description languages to C/C++, but high-quality results still depend on schedule decisions such as pipelining, unrolling, tiling, reordering, and buffering. These decisions create a combinatorial design space, while many numerical kernels exhibit recurring computation patterns that suggest different optimization strategies. This paper presents PATTERNDSE, a lightweight pattern-guided design space exploration (DSE) framework for FPGA kernels written in Allo, a scheduling-oriented HLS programming system. PATTERNDSE maps recurring computation patterns, including elementwise maps, reductions, matrix-vector operations, matrix-matrix operations, and stencil-like updates, to compact schedule spaces. It then applies candidate schedules, validates functional correctness through LLVM execution, checks HLS C code generation, and uses a simple pattern-aware estimator to rank candidates before Vitis HLS synthesis. We evaluate PATTERNDSE on six representative kernels: vecadd, axpy, dot, matvec, gemm, and jacobi2d. Compared with an exhaustive-lite baseline, pattern-guided DSE reduces the number of HLS-evaluated candidates from 140 to 29, achieving a 4.83x overall search reduction and up to 12.0x reduction for individual kernels. Across all evaluated kernels, PATTERNDSE recovers the same best valid Vitis HLS latency as the exhaustive-lite baseline, demonstrating that computation-pattern information can prune unproductive schedule combinations while preserving high-quality HLS outcomes.

cs.AR↗

arXiv2025

Stiff Circuit System Modeling via Transformer

Accurate and efficient circuit behavior modeling is a cornerstone of modern electronic design automation. Among different types of circuits, stiff circuits are challenging to model using previous frameworks. In this work, we propose a new approach using Crossformer, which is a current state-of-the-art Transformer model for time-series prediction tasks, combined with Kolmogorov-Arnold Networks (KANs), to model stiff circuit transient behavior. By leveraging the Crossformer's temporal representation capabilities and the enhanced feature extraction of KANs, our method achieves improved fidelity in predicting circuit responses to a wide range of input conditions. Experimental evaluations on datasets generated through SPICE simulations of analog-to-digital converter (ADC) circuits demonstrate the effectiveness of our approach, with significant reductions in training time and error rates.

cs.CE↗

arXiv2025

Toward Engineering AGI: Benchmarking the Engineering Design Capabilities of LLMs

Modern engineering, spanning electrical, mechanical, aerospace, civil, and computer disciplines, stands as a cornerstone of human civilization and the foundation of our society. However, engineering design poses a fundamentally different challenge for large language models (LLMs) compared with traditional textbook-style problem solving or factual question answering. Although existing benchmarks have driven progress in areas such as language understanding, code synthesis, and scientific problem solving, real-world engineering design demands the synthesis of domain knowledge, navigation of complex trade-offs, and management of the tedious processes that consume much of practicing engineers' time. Despite these shared challenges across engineering disciplines, no benchmark currently captures the unique demands of engineering design work. In this work, we introduce EngDesign, an Engineering Design benchmark that evaluates LLMs' abilities to perform practical design tasks across nine engineering domains. Unlike existing benchmarks that focus on factual recall or question answering, EngDesign uniquely emphasizes LLMs' ability to synthesize domain knowledge, reason under constraints, and generate functional, objective-oriented engineering designs. Each task in EngDesign represents a real-world engineering design problem, accompanied by a detailed task description specifying design goals, constraints, and performance requirements. EngDesign pioneers a simulation-based evaluation paradigm that moves beyond textbook knowledge to assess genuine engineering design capabilities and shifts evaluation from static answer checking to dynamic, simulation-driven functional verification, marking a crucial step toward realizing the vision of engineering Artificial General Intelligence (AGI).

cs.CE↗