TensorFlow Lite Micro for Espressif Chipsets

LiteRT is the new name for TensorFlow Lite (TFLite). While the name is new, it's still the same trusted, high-performance runtime for on-device AI, now with an expanded vision.

🔥🔥「InterviewGuide」是阿秀从校园->职场多年计算机自学过程的记录以及学弟学妹们计算机校招&秋招经验总结文章的汇总，包括但不限于C/C++ 、Golang、JavaScript、Vue、操作系统、数据结构、计算机网络、MySQL、Redis等学习总结，坚持学习，持续成长！

A version manager for neovim

课程视频、PPT和源代码：侯捷C++系列；台大郭彦甫MATLAB

小彭老师领衔编写，现代C++的中文百科全书

润学全球官方指定GITHUB，整理润学宗旨、纲领、理论和各类润之实例；解决为什么润，润去哪里，怎么润三大问题； 并成为新中国人的核心宗教，核心信念。

🧡 Everything is RSSible

Classification of Human Movement using mmWave FMCW Radar Micro-Doppler Signature

Using mmWave radars for some computer vision tasks.

Pythonic mmWave Toolbox for TI's IWR Radar Sensors

A catkin workspace in ROS which uses DBSCAN to identify which points in a point cloud belong to the same object.

2021年秋季学期 南京大学ICS课程 PA实验部分

Boost your terminal, script by script

A point cloud dataset for mmWave radar human activity recognition.

PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space

A curated list of neural network pruning resources.

OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 2…

[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning

Sparsity-aware deep learning inference runtime for CPUs

Awesome LLM compression research papers and tools.

EE-LLM is a framework for large-scale training and inference of early-exit (EE) large language models (LLMs).

For releasing code related to compression methods for transformers, accompanying our publications

Awesome list for LLM pruning.

Automated Identification of Redundant Layer Blocks for Pruning in Large Language Models

Unofficial implementations of block/layer-wise pruning methods for LLMs.

Structural Pruning for LLaMA

[ICLR 2024] Jaiswal, A., Gan, Z., Du, X., Zhang, B., Wang, Z., & Yang, Y. Compressing llms: The truth is rarely pure and never simple.