Solutions Resources
If you’re designing and developing for the Internet of Things, you live in a low-throughput, low-power world.
Advances in video conference technologies let users collaborate across geographies, time zones, and even pandemics. But great experiences happen when every attendee feels immersed in the meeting, and their voices are heard.
Sample Application for Image Enhancement Introduction Pre-requisites Model Selection, and DLC Conversion Model Overview Steps to convert model to DLC Source Overview Model Initialization Results References...
Sample App for Image Super Resolution Introduction Pre-requisites Model Architecture and Conversion Model Overview Steps to convert model to DLC Source Overview Model Initialization Results References
Introduction...
Object Detection with MobilenetSSD Introduction Pre-requisites Model Architecture and Conversion How to get the Model? How to change the Model? Source Overview Code Implementation Installing the Demo References...
On Device Sentiment-Analysis with Transformers Introduction Prerequisites Model Preparation and Validation Model Preparation Model and DLC Validation On Device Profiling Build and Run with Android Studio Qualcomm Neural Processing...
On Device Question-Answering with Transformers Introduction Prerequisites Model Preparation and Validation Model Preparation Model and DLC Validation On Device Profiling Build and Run with Android Studio Qualcomm®...
The following solutions were designed to demonstrate the capability of the development kit. Solutions are hosted on GitHub repository here.
This repository contains sample android applications, which are designed to use components from the following products Qualcomm Neural Processing SDK...
1.1 TensorFlow Installation:
pip install tensorflow==2.3.0
1.2 TF-Lite Installation:
pip install tflite==2.3.0
1.3 ONNX Installation:
pip install onnx==1.6.0
Along with ONNX, install the Onnxsim and Onnxruntime packages with commands mentioned below
pip install onnxsim
pip install...
The following steps will walk you through the installation of Qualcomm® Neural Processing SDK. These steps have been tested on Version 2.5.x.
SDK is supported only on Ubuntu 18.0.4.
Configure nameserver
echo "nameserver 8.8.8.8" | sudo tee /etc/resolv....
Introduction Setting up the development kit Build demos from source Pre-requisites Compile and Run demo from source code Re-build sample application with newer version of Qualcomm Neural Processing SDK HDMI display output and Emergency build loading...
Introduction AI Hardware Cores/Accelerator Overview AI Software Accelerator Framework Qualcomm® Neural Processing SDK AIMET - AI Model Efficiency Toolkit
Introduction
Below are a few terms and concepts that you may come across while using Artificial...
Today, edge devices are everywhere, with tens of billions more expected to be deployed in the coming years.
What do on-demand language translation, self-driving cars, and video calls have in common? They’re all examples of today’s ubiquitous technology that was once the figment of science fiction (sci-fi).
For Linux developers who build low-level kernel and driver solutions, the open-source development model plays
We have conversations all the time with people trying to understand the business value of combining cloud computing and edge computing.
The international embedded computing community marked its 20th anniversary with this year’s Embedded World conference in Nuremberg.
The World Health Organization reports that over 50% of vaccines worldwide are wasted.
Today’s intelligent IoT edge devices can utilize cellular and Wi-Fi to connect with the cloud for reliable high-bandwidth and low-latency connectivity. But incorporating wireless connectivity into your IoT device is more involved than simply integrating a radio module.