There is a summary repo for collecting awesome Efficient AI papers. If you want to contribute to this repo, feel free to pr(pull request)!
- Efficient Large Language Models: A Survey, Arxiv, Github repo
- TinyML and Efficient Deep Learning @MIT by Prof. Song Han (I may update some my learning notes later on my homepage)
- NNPACK
- DMLC: Tensor Virtual Machine (TVM): Open Deep Learning Compiler Stack
- Tencent: NCNN
- Xiaomi: MACE, Mobile AI Benchmark
- Alibaba: MNN blog (in Chinese)
- Baidu: Paddle-Slim, Paddle-Mobile, Anakin
- Microsoft: ELL, AutoML tool NNI
- Facebook: Caffe2/PyTorch
- Apple: CoreML (iOS 11+)
- Google: ML-Kit, NNAPI (Android 8.1+), TF-Lite
- Qualcomm: Snapdragon Neural Processing Engine (SNPE), Adreno GPU SDK
- Huawei: HiAI
- ARM: Tengine
- Related: DAWNBench: An End-to-End Deep Learning Benchmark and Competition
- Awesome-NAS
- Awesome-Pruning
- Awesome-Knowledge-Distillation
- MS AI-System open course
- caffe-int8-convert-tools
- Neural-Networks-on-Silicon
- Embedded-Neural-Network
- model_compression
- model-compression (in Chinese)
- Efficient-Segmentation-Networks
- AutoML NAS Literature
- Papers with code
- ImageNet Benckmark
- Self-supervised ImageNet Benckmark
- NVIDIA Blog with Sparsity Tag