自监督、预训练、跨阶段对齐的电路编码器为各种设计任务提供了基础.pdf

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1、A Self-Supervised,Pre-Trained,and Cross-Stage-Aligned Circuit Encoder Provides a Foundation for Various Design Tasks1Wenji Fang1,Shang Liu1,Hongce Zhang1,2,Zhiyao Xie1wfang838connect.ust.hk1Hong Kong University of Science and Technology2Hong Kong University of Science and Technology(Guangzhou)2Outli

2、ne Background CircuitEncoder Framework Experimental Results Conclusion&Future WorkBackground4Background:AI for EDA Remarkable achievements Design quality evaluation Power,timing,area,routability,etc.Functionality reasoning Arithmetic word-level abstraction,SAT,etc.Optimization Design space explorati

3、on,etc.Generation RTL code,verification,etc.5Background:AI for EDA Most existing predictive solutions are task-specific Supervised methods:tedious and time-consuming Hard to generalize to other tasks6Background:Foundation Models AI foundation models Pretrain-finetune paradigm Pre-training on large a

4、mounts of unlabeled data(self-supervised)Fine-tuning based on task-specific labels(supervised)Applications Natural language processing:GPT,BERT,Llama,etc.Computer vision:DALLE,stable-diffusionCircuitEncoder Framework8Motivation:Towards Circuit Foundation Models Large circuit model9Motivation:Towards

5、 Circuit Foundation Model Our targeted circuit foundation model Capture unique circuit intrinsic property Cross-stage:RTL(functional)netlist(Physical)Equivalent transformation:semantic&structure Support various types of tasks Functionality:reasoning,verification,etc.Design quality:performance,power,

6、area,etc.10Key Idea:First RTL-Netlist Cross-Stage Alignment General circuit foundation model solution Self-supervised pre-trained:circuit graph function contrastive Cross-stage aligned:RTL(func.)netlist(phys.)alignment Support various design tasks:Lightweight downstream task model PPA+functionality1