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cs.AR2026
Decoding the Skew: Distribution-Aware MoE Inference with Adaptive Kernel Dispatch
En-Ming Huang, An-Cheng Chang, Bai-Cheng Jeng +2
Mixture-of-Experts (MoE) inference consists of sparse expert GEMMs whose shapes vary with the runtime routing distribution. Existing serving systems typically select fused-MoE kern…
cs.AR2026
Exploring LLM-based Verilog Code Generation with Data-Efficient Fine-Tuning and Testbench Automation
Mu-Chi Chen, Po-Hsuan Huang, Yu-Hung Kao +6
Recent advances in large language models have improved code generation, but their use in hardware description languages is still limited. Moreover, training data and testbenches fo…
cs.AR2026
SiliconMind-V1: Multi-Agent Distillation and Debug-Reasoning Workflows for Verilog Code Generation
Mu-Chi Chen, Yu-Hung Kao, Po-Hsuan Huang +10
Large language models (LLMs) have recently emerged as a promising approach for automating Verilog code generation; however, existing methods primarily emphasize syntactic correctne…