6 papers
ChipVerilog: A Large-Scale OpenCores-Derived Benchmark for LLM-Based Verilog RTL Generation
Yan Tan, Jiping Du, Xiangchen Meng +1
Large language models have shown strong potential for Verilog RTL generation. However, many existing benchmarks are built from short, self-contained module-level tasks. These tasks…
VerilogCL: A Contrastive Learning Framework for Robust LLM-Based Verilog Generation
Yan Tan, Tong Liu, Xiangchen Meng +1
Large Language Models (LLMs) have recently achieved strong performance in software code generation. However, applying them to hardware description languages (HDLs), such as Verilog…
CellE: Automated Standard Cell Library Extension via Equality Saturation
Yi Ren, Yukun Wang, Xiang Meng +6
Automated standard cell library extension is crucial for maximizing Quality of Results (QoR) in modern VLSI design. We introduce CellE, a novel framework that leverages formal meth…
AutoVeriFix+: High-Correctness RTL Generation via Trace-Aware Causal Fix and Semantic Redundancy Pruning
Yan Tan, Xiangchen Meng, Zijun Jiang +1
Large language models (LLMs) have demonstrated impressive capabilities in generating software code for high-level programming languages such as Python and C++. However, their appli…
FedBit: Accelerating Privacy-Preserving Federated Learning via Bit-Interleaved Packing and Cross-Layer Co-Design
Xiangchen Meng, Yangdi Lyu
Federated learning (FL) with fully homomorphic encryption (FHE) effectively safeguards data privacy during model aggregation by encrypting local model updates before transmission,…
AutoVeriFix: Automatically Correcting Errors and Enhancing Functional Correctness in LLM-Generated Verilog Code
Yan Tan, Xiangchen Meng, Zijun Jiang +1
Large language models (LLMs) have demonstrated impressive capabilities in generating software code for high-level programming languages such as Python and C++. However, their appli…