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Compiler Optimization
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Machine learning guided compiler optimization
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alibaba/EasyNLP
공식 코드EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
https://github.com/baco-authors/baco
공식 코드BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
https://github.com/cda-tum/mqtpredictor
공식 코드Compiler Optimization for Quantum Computing Using Reinforcement Learning
https://github.com/facebookresearch/CompilerGym
공식 코드CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
https://github.com/iamawhalez/fun2vec
공식 코드SimCLF: A Simple Contrastive Learning Framework for Function-level Binary Embeddings
https://github.com/ise-uiuc/whitefox
공식 코드WhiteFox: White-Box Compiler Fuzzing Empowered by Large Language Models
https://github.com/longhao-chen/aicas2024
공식 코드https://github.com/snu-arc/ginex
공식 코드https://github.com/taehokim20/cprune
공식 코드CPrune: Compiler-Informed Model Pruning for Efficient Target-Aware DNN Execution
https://github.com/Anna-Bele/LLM_MCTS_Search
Compiler Optimization via LLM Reasoning for Efficient Model Serving
https://github.com/alibaba/EasyTransfer
EasyTransfer -- A Simple and Scalable Deep Transfer Learning Platform for NLP Applications
https://github.com/cda-tum/mqt-predictor
Compiler Optimization for Quantum Computing Using Reinforcement Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.