This course is designed for beginner AI developers, ML engineers, data scientists, and mobile developers looking to deploy optimized models on edge devices

A curated list of awesome embedded programming.

This is the repository for my research internship with the Embedded Systems Group at TU Delft, based on Junyu Lu's master thesis - https://github.com/Eagleggs/Split_inference_microcontrollers

This repository deals with the notion of applied Tiny Machine Learning and research for Good, where Good represents {Healthcare, Climate change, Security and further}.

This project focuses on developing a method to measure human height using ultrasound sensors under challenging conditions caused by dense scalp hair.

Speech Recognition using STM32 and Machine Learning

NPRACH UAD Neural Network implementation in Fixed point C.

Predict wine quality using a deep neural network model and STM Embedded AI

This is an open source project on the deployment of deep learning to embedded microprocessors. The project establishes a data set for obstacle recognition of blind travel environment, and trains a simplified MoblieNet model in TensorFlow. Finally, the binary file of the model is deployed on the UNCLEO-STM32H7A3ZIT-Q development board to realize …

Here we present the trained model and the algorithm that let us win the ICIAP2023 ONFIRE contest

Guide to deploying neural networks in VST plugins, with a specific focus on embedded devices using the Elk Audio OS

JUCE Template plugins to use TensorFlow lite for deep learning inference

📝 This project attempts to offer a new way to write that makes use of a specific tool that can detect the shapes you create with your hand in the air.

Embedded and mobile deep learning research resources

Transfer Learning and Embedded AI with Pytorch

MicroAI™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is industrial machinery, small devices or the like).

MicroAI™ is an AI engine that can operate on low power edge and endpoint devices. It can learn the pattern of any and all time series data and can be used to detect anomalies or abnormalities, make one step ahead predictions/forecasts, and calculate the remaining life of entities (whether it is industrial machinery, small devices or the like).

This project focuses on the implementation of optimized Linear and DNN regression models for inter-vehicle distance prediction in a Cooperative Adaptive Cruise Control (CACC) application. It leverages Tensorflow Lite to create optimized models through quantization and pruning for realtime inferencing on Raspberry Pi and On-board Unit (OBU) of Co…

benchmark for embededded-ai deep learning inference engines, such as NCNN / TNN / MNN / TensorFlow Lite etc.