GPU-Accelerated AI & Vision Systems

Scalable real-time AI using GPUs, edge devices, and clustering
Subtopics:

Faster R-CNN for high-resolution detection

GPU-based unsupervised image clustering

Contour extraction at scale

SEM image processing & inline defect detection

Edge/cloud integration

My Contribution: My work drives the cutting edge of Artificial Intelligence by harnessing the immense computational power of GPUs to transform computer vision and image processing. I develop innovative algorithms and systems for autonomous image segmentation, unsupervised clustering, and smart pattern recognition, achieving dramatic performance enhancements for real-time AI processing. My research is pivotal for high-performance computing (HPC) scalability in big data environments, significantly boosting efficiency in critical applications like semiconductor manufacturing and product lifecycle management.

Publications & Patents:

GPU-Accelerated Feature Extraction for Real-Time Vision AI and LLM Systems Efficiency: Autonomous Image Segmentation, Unsupervised Clustering, and Smart Pattern Recognition for…

K. Ahi
2025 36th Annual SEMI Advanced Semiconductor Manufacturing Conference (ASMC…)
2025
Cited by: (New publication, citations building)
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Highlight: This research pioneers methods for significantly accelerating Vision AI and LLM systems through GPU-powered feature extraction, crucial for real-time applications and enhanced efficiency.
GPU-Accelerated Feature Extraction for Vision AI: Autonomous Image Segmentation & Smart Pattern Recognition for Scalable Real-Time AI Processing with 6.6× Faster Performance…

K. Ahi
IEEE ASMC
2025
Cited by: (New publication, citations building)
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Highlight: This publication details a groundbreaking approach to achieve 6.6x faster real-time Vision AI processing through GPU-accelerated autonomous image segmentation and smart pattern recognition.
AI-Powered End-to-End Product Lifecycle: Boosting Reviewer Productivity by 80%+ and Accelerating Decision-Making via Real-Time Anomaly Detection and Data Refinement with GPU…

K. Ahi
SPIE Silicon Valley
2025
Cited by: (New publication, citations building)
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Highlight: This work introduces an AI-powered framework that leverages GPU acceleration to drastically enhance product lifecycle management, achieving over 80% increase in reviewer productivity through real-time anomaly detection.
Advancing AI-Driven Computer Vision and Image Segmentation via Pattern Recognition, GPU-Accelerated Unsupervised Clustering, and Edge AI for HPC-Scalable Big Data Processing…

K. Ahi
SPIE Silicon Valley
2025
Cited by: (New publication, citations building)
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Highlight: This paper presents advancements in AI-driven computer vision, utilizing GPU-accelerated unsupervised clustering and edge AI for scalable big data processing and image segmentation.
Self-supervised deep learning neural network for CD-SEM image denoising using reduced dataset

K. Ahi
Metrology, Inspection, and Process Control XXXVII 12496, 365-378
2023
Cited by: 7
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Highlight: This research introduces a self-supervised deep learning neural network for efficient image denoising, critical for high-precision metrology in semiconductor manufacturing.