The Qualcomm Developer Network hired venTAJA to work on a series of blog posts describing how Qualcomm’s family of processors can accelerate AI using the graphics processing unit (GPU) and neural processing unit (NPU).

AnythingLLM – Local AI optimized for CPU and NPU on Snapdragon X Series Devices • Use C++ in Your Machine Language Programming with Hexagon SDK 3.1 • Model HQ by LLMWare.ai: Run language models and use AI agents on Snapdragon X Series devices

Basis Technology’s Rosette text analytics platform uses classical machine learning and deep neural nets to extract meaningful information from unstructured data. Basis engaged venTAJA for a series of white papers on the technical details of its name matching feature.

Understanding Match Scoring in Rosette

TIBCO found that many of its customers were developing and training machine learning (ML) models yet not getting them into production. The company chose venTAJA for an eBook on how to build, manage, deploy/integrate and monitor ML models successfully.

ML Ops: Operationalizing Data Science

As processor performance increases, developers can move more of the computational work of artificial intelligence (AI) from the cloud down to mobile devices. venTAJA worked on learning resources and blog posts for the Qualcomm Developer Network to introduce programmers to running workloads more efficiently among the CPU, GPU and DSP on mobile devices at the network edge.

Neural Processing learning resources • Ollama simplifies AI inference with open-source models

Enterprises use Quest’s software products for governance of their data, and the company has followed that market into AI governance. Quest engaged venTAJA to work on blog posts from subject matter experts on governance.

Developing an AI governance framework: Components and considerations

To showcase a developer engagement featuring its facial processing and facial recognition technologies, the Qualcomm Developer Network asked venTAJA to write a case study on Magisto’s CamCrew app. They also commissioned a demonstration guide for the app, from which a related video script evolved.

Magisto CamCrew Video Script

Qualcomm engineers heard from developers keen to accelerate matrix multiply (MM) operations for convolutional neural networks in their deep learning (DL) apps. The Qualcomm Developer Network engaged venTAJA to work on an application note as a series of blog posts about accelerating deep learning on Qualcomm’s Adreno GPU.

Matrix Multiply for Deep Learning on Adreno GPUs –

Part 1: OpenCL Optimization • Part 2: Host Code and Kernel