Paper

Abstract

这里用来放读论文的笔记，写的不好还请见谅>_<|||

TODO

• 重构页面，对论文分类
• T-MAC + BitNet 系列
• CoT
• InstructGPT
• 量化系列（Atom、QuaRot）
• 投机解码

• BiS-KM: Enabling Any-Precision K-Means on FPGAs
• ZeRO: Memory Optimizations Toward Training Trillion Parameter Models
• Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism
• GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism
• Mixed Precision Training
• FAST: DNN Training Under Variable Precision Block Floating Point with Stochastic Rounding
• Softmax Acceleration with Adaptive Numeric Format for both Training and Inference
• Efficient Memory Management for Large Language Model Serving with PagedAttention
• VecPAC: A Vectorizable and Precision-Aware CGRA
• NN-LUT: Neural Approximation of Non-Linear Operations for Efficient Transformer Inference
• ChatEDA: A Large Language Model Powered Autonomous Agent for EDA
• Improving Language Understanding by Generative Pre-Training
• Language Models are Unsupervised Multitask Learners
• Language Models are Few-Shot Learners
• APEX: A Framework for Automated Processing Element Design Space Exploration using Frequent Subgraph Analysis
• QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
• Learning Transferable Visual Models From Natural Language Supervision
• LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders

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