README.md

important articles for personal, especially on computer science.

1. deep learning

• tensorflow lite micro

  • TensorFlow Lite Micro: Embedded Machine Learning on TinyML Systemsa
  • https://arxiv.org/abs/2010.08678

• cnn-winograd

  • Fast Algorithms for Convolutional Neural Networks
  • https://arxiv.org/abs/1509.09308

• how to optimize gemm

  • https://github.com/flame/how-to-optimize-gemm/wiki

• separable convolutions---depthwiseconv and pointwiseconv

  • A Basic Introduction to Separable Convolutions
    • https://towardsdatascience.com/a-basic-introduction-to-separable-convolutions-b99ec3102728
  • MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
    • https://arxiv.org/abs/1704.04861
    • it used separable conv in mobilenet, so it introduced separable conv in this article.

• binary neural networks

  • https://github.com/JDAI-CV/dabnn
  • daBNN: A Super Fast Inference Framework for Binary Neural Networks on ARM devices
    • https://arxiv.org/abs/1908.05858

• On the Measure of Intelligence

  • https://arxiv.org/abs/1911.01547

2. memory

• What Every Programmer Should Know About Memory
  • https://people.freebsd.org/~lstewart/articles/cpumemory.pdf
• Carnegie Mellon University
  • 11-memory-hierarchy.pdf
    • https://www.cs.cmu.edu/afs/cs/academic/class/15213-f15/www/lectures/11-memory-hierarchy.pdf
  • 12-cache-memories.pdf
    • https://www.cs.cmu.edu/afs/cs/academic/class/15213-f15/www/lectures/12-cache-memories.pdf

3. quantification

• 2021, A White Paper on Neural Network Quantization
  • https://arxiv.org/abs/2106.08295
• Quantizing deep convolutional networks for efficient inference: A whitepaper
  • https://arxiv.org/abs/1806.08342
• Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
  • https://arxiv.org/abs/1712.05877
• Outlier Channel Splitting
  • Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
    • https://arxiv.org/abs/1901.09504 , three methods abou clip
  • Outlier Channel Splitting Improving DNN Quantization without Retraining
    • https://icml.cc/media/icml-2019/Slides/5053.pdf
    • github, https://github.com/cornell-zhang/dnn-quant-ocs
• nvidia, INTEGER QUANTIZATION FOR DEEP LEARNING INFERENCE: PRINCIPLES AND EMPIRICAL EVALUATION
  • https://arxiv.org/pdf/2004.09602.pdf, show some interesting data
• DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
  • https://arxiv.org/abs/1606.06160

4. LLM

• Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
  • https://arxiv.org/abs/2312.15234
• stable diffusion 3, Scaling Rectified Flow Transformers for High-Resolution Image Synthesis
  • https://stabilityai-public-packages.s3.us-west-2.amazonaws.com/Stable+Diffusion+3+Paper.pdf
• BiTA: Bi-Directional Tuning for Lossless Acceleration in Large Language Models
  • https://arxiv.org/html/2401.12522v2
  • https://github.com/linfeng93/BiTA
• deploy:
  • source
    • vllm: https://github.com/vllm-project/vllm
    • lightllm: https://github.com/ModelTC/lightllm
    • mlc-llm: https://github.com/mlc-ai/mlc-llm
• kv cache
  • paged attention
    • paged attention: both vllm and lightllm(token attention) used.
    • Efficient Memory Management for Large Language Model Serving with PagedAttention
      • https://arxiv.org/abs/2309.06180
    • https://blog.vllm.ai/2023/06/20/vllm.html
  • qcom-qualla: NSPKVManager
  • llama, ggml_cpy, just for memory copy and concat
  • MQG, GQA, need to change model struct.
    • https://arxiv.org/pdf/2305.13245 GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
    • https://zhuanlan.zhihu.com/p/655325832
    • https://zhuanlan.zhihu.com/p/659238103
• quantize
  • aimet, AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
    • https://github.com/quic/aimet
  • qserve mit, QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving
    • https://arxiv.org/abs/2405.04532
    • https://github.com/mit-han-lab/qserve

5. others

• Latency Numbers Every Programmer Should Know

  • https://colin-scott.github.io/personal_website/research/interactive_latency.html
  • github ， https://github.com/colin-scott/interactive_latencies

• DEN0018A_neon_programmers_guide_en

  • https://www.mobibrw.com/wp-content/plugins/pdfjs-viewer-shortcode/pdfjs/web/viewer.php?file=/wp-content/uploads/2019/04/DEN0018A_neon_programmers_guide_en.pdf&attachment_id=&dButton=true&pButton=true&oButton=false&sButton=true
  • https://gist.github.com/csukuangfj/ff116f9296b4d3f661e2d42e5da18d01

• 硬件体系架构浅析

  • https://lrita.github.io/images/posts/tools/%E7%A1%AC%E4%BB%B6%E4%BD%93%E7%B3%BB%E6%9E%B6%E6%9E%84%E6%B5%85%E6%9E%90.pdf

• google, tpu, In-Datacenter Performance Analysis of a Tensor Processing Unit

  • https://arxiv.org/abs/1704.04760