Awesome AI Accelerators

A curated list of papers, tools, and resources for AI accelerators — spanning the entire stack from silicon to serving systems.

AI accelerators are the engines behind modern machine learning — the GPUs, TPUs, NPUs, and custom ASICs that make training and inference practical at scale. Building and programming them is a deeply interdisciplinary craft that spans deep learning, computer architecture, compilers, GPU programming, RTL design, and physical implementation.

This list is a curated, opinionated map of that landscape. It favors canonical papers and widely adopted open-source projects over exhaustive coverage. Whether you are an undergraduate taking your first architecture course, a PhD student researching dataflow architectures, or an engineer optimizing kernels at a frontier lab, the goal is to give you the best resource for each topic — and a clear path through the rest.

Contents

• AI Foundations
  • AI Fundamentals
  • Deep Learning
  • Transformers & LLMs
  • Diffusion Models
  • Vision Models
  • Reinforcement Learning
• AI Inference
  • Quantization
  • KV Cache
  • Speculative Decoding
  • Prefix Caching
  • Continuous Batching
  • Mixture of Experts
  • Sparsity & Pruning
  • Scheduling
  • Memory Optimization
  • Serving Systems
• GPU Programming
  • CUDA
  • Triton
  • ROCm
  • Metal
• AI Kernels
  • GEMM
  • FlashAttention
  • PagedAttention
  • Normalization & Softmax
  • RoPE & Positional Encodings
  • Fused Operators
  • Kernel Optimization Guides
• Runtime Systems
• Compilers
• Computer Architecture
  • Foundations
  • Memory Systems & Cache
  • [Inte