Stars
A project demonstrating how to use nvmetamux to run multiple models in parallel.
A collection of pre-trained, state-of-the-art models in the ONNX format
Description of Work Parallelizing GPU and DLA in NVIDIA Embedded Boards (2022)
Description of Framework for Efficient Fused-layer Cost Estimation, Legion (2021)
Object Detection Implementation with NNStreamer (2020)
Batch Partitioning for Multi-PE Inference with TVM (2020)
DeepMIMO (v2 & v3 Matlab) Dataset Framework for mmWave and massive MIMO Research
Deep neural network library and toolkit to do high performace inference on NVIDIA jetson platforms
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies.
Unlock your displays on your Mac! Flexible HiDPI scaling, XDR/HDR extra brightness, virtual screens, DDC control, extra dimming, PIP/streaming, EDID override and lots more!
debauchee / barrier
Forked from deskflow/deskflowOpen-source KVM software
Open-source simulator for autonomous driving research.
Remote source nodes for NNStreamer pipelines without GStreamer dependencies
Example applications of nnstreamer. Note that we have to enable the 'apptest" CI module in the near future.
Autodidactic Neurosurgeon Collaborative Deep Inference for Mobile Edge Intelligence via Online Learning
World's fastest ANPR / ALPR implementation for CPUs, GPUs, VPUs and NPUs using deep learning (Tensorflow, Tensorflow lite, TensorRT, OpenVX, OpenVINO). Multi-Charset (Latin, Korean, Chinese) & Mult…
AddressSanitizer, ThreadSanitizer, MemorySanitizer
Keras package for region-based convolutional neural networks (RCNNs)
R-CNN: Regions with Convolutional Neural Network Features
inference on tvm runtime using c++ with gpu enabled
An Open Source Machine Learning Framework for Everyone
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
mean Average Precision - This code evaluates the performance of your neural net for object recognition.
🔀 Neural Network (NN) Streamer, Stream Processing Paradigm for Neural Network Apps/Devices.