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

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

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

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

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

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

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.

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

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

C implementation of a SHA-1 cracker with various optimizations

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

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%