early-exit

There are 13 repositories under early-exit topic.

  • IntelLabs/distiller

    Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller

    Language:Jupyter Notebook4.4k130350805

  • facebookresearch/LayerSkip

    Code for "LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding", ACL 2024

    Language:Python335101630

  • falcon-xu/early-exit-papers

    A curated list of early exiting (LLM, CV, NLP, etc)

    62104

  • dywsjtu/apparate

    Artifact for "Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving" [SOSP '24]

    Language:Python25212

  • falcon-xu/LGViT

    Official PyTorch implementation of "LGViT: Dynamic Early Exiting for Accelerating Vision Transformer" (ACM MM 2023)

    Language:Python11142

  • fangvv/EdgeKE

    Code for paper "EdgeKE: An On-Demand Deep Learning IoT System for Cognitive Big Data on Industrial Edge Devices"

    Language:Python9203

  • Shikha-code36/early-exit-cnn

    A deep learning framework that implements Early Exit strategies in Convolutional Neural Networks (CNNs) using Deep Q-Learning (DQN). This project enhances computational efficiency by dynamically determining the optimal exit point in a neural network for image classification tasks on CIFAR-10.

    Language:Jupyter Notebook7100

  • hpclab/learning-exit-strategies-ensembles

    Official repository of Busolin et al., "Learning Early Exit Strategies for Additive Ranking Ensembles", ACM SIGIR 2021.

    Language:Jupyter Notebook6600

  • chbtt/sha1-cracker

    C implementation of a SHA-1 cracker with various optimizations

    Language:C1000

  • giulio-derasmo/Experimenting-with-modularity-in-deep-learning

    The project aim to experiment implementing a modular architecture: an early-exit model and testing it using Tensorflow.

    Language:Jupyter Notebook1100

  • ywuwuwu/Early-Exit-Papers

    This repository is dedicated to self-learning about early exit papers, including relevant code and documentation.

    0100

  • hskim0584/branchy_net_cnn_Automatic_leftturn_sidemirror

    Language:Python

  • Sottix99/Leaf-Disease-Classification

    This project focuses on the automatic classification of corn leaf diseases using deep neural networks. The dataset includes over 4000 images categorized into four classes: Common Rust, Gray Leaf Spot, Blight, and Healthy. Through the use of Convolutional Neural Networks and advanced techniques, the model achieves a classification accuracy of 91.5%

    Language:Jupyter Notebook10