Masoud Nouripayam defends his thesis "Near-Memory Computing Architectures for Scalable Edge AI Applications."

Zoom link.
Zoom ID: 69455644174

Link to the thesis i LU Research Portal.

Artificial intelligence (AI) and machine learning (ML) are rapidly permeating nearly every aspect of modern life, from personal devices and autonomous systems to industrial automation and environmental monitoring. The growing demand for intelligence at the network edge is reshaping how computing hardware is conceived and built. Edge AI platforms are expected to deliver high throughput within tight energy and area budgets, operate reliably at low voltages, and adapt to diverse workloads, while data movement between processors and memory continues to dominate system cost. These trends position memory-centric computing as a compelling alternative to conventional architectures. By tightly coupling local memory, approximate processing, and near-memory execution, this work advances the development of compact, high-throughput, and energy-efficient AI hardware.