Image Stitching — Qualcomm Edge Deployment
Jan 1, 2023
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1 min read
Project Details
01
Model Conversion
Transformed a feature-detection deep learning model into ONNX and Qualcomm target-device formats.
02
C++ Integration
Created a C++ pipeline combining deep learning feature detection and matching.
03
Realtime Deployment
Deployed the pipeline on Qualcomm hardware for realtime edge processing.
04
Performance Gain
Reduced CPU runtime by 75% through edge pipeline optimization.
Tech Stack
ML & Debugging
Python
NumPy
PyTorch
OOP
PDB Debugger
Edge & Systems
C++
ONNX
QNN
VS Code
Linux
Authors
Lead Engineer — GenAI / Agentic AI & Applied ML
Lead Engineer with 5 years of hands-on experience developing and deploying GenAI, RAG, Agentic AI, and deep learning systems from scratch. I build production-grade AI platforms for realtime voice interaction, enterprise knowledge retrieval, regulatory search, and automated finance analysis. My work spans NLP, autonomous driving, computer vision, and Camera-LiDAR-Radar fusion, with a focus on reliable architecture, model optimization, and scalable data pipelines.
Stack: Python, GCP, GenAI, LLMs, Agents, Docker, FastAPI, Cloud SQL, Google ADK, Vertex AI, Agent Engine, Cloud Run, OpenAI Realtime API, WebSockets, Redis, MongoDB, Milvus, JWT, Docling, BM25, ModernBERT, vLLM, and FAISS.