Masoud förbättrade datorkretsar

Describe your research in a popular science way 

Energy, efficiency, and environmental impact are among the most critical concerns guiding every step of technological advancement. Artificial intelligence is expanding into nearly every aspect of daily life and requires access to and processing of huge amounts of data, which leads to increasingly resource-demanding workloads. Deploying AI on battery-powered devices with limited energy and memory makes it essential to address energy efficiency directly at the chip level and rethink conventional computing architectures instead of relying on traditional approaches that only attempt to mitigate performance and energy bottlenecks. My research focuses on enhancing future AI devices to deliver higher performance with lower energy demand, avoiding the waste of time and power in conventional computing architectures. In conventional systems, data is constantly shuttled between processors and memory, which results in unnecessary energy consumption and performance loss. The developed approach enables memory to perform parts of the computation close to where the data already resides. This data-centric computing concept, known as near-memory computing, can make everything from small sensors to advanced edge AI systems significantly more efficient.

What made you want to pursue a PhD?

I was very eager to understand the underlying principles and truly learn how computing systems work. I have always been ambitious to shape technology and to contribute meaningfully to the driving force that pushes it forward. Pursuing a PhD gave me the opportunity to explore ideas in depth and work on innovations that can make a real impact in the future.

What is the most fascinating or interesting with your thesis subject?

What fascinates me most is how relatively small architectural changes near memory can lead to dramatic improvements in both performance and energy efficiency. Even subtle shifts in where and how computations are carried out can unlock significant system-level benefits. This demonstrates that meaningful innovation does not always depend on inventing entirely new technologies. In many cases, the key lies in rethinking and reengineering existing technologies in smarter, more efficient ways, allowing us to push the boundaries of what current hardware can achieve.

Do you believe some results from your research will be applied in practice eventually? And if so, how / how?

In the past few years, near-memory computing has started to gain significant traction, and researchers from different disciplines have contributed to realizing and improving this concept even further. I believe that in the near future, commercial products will be equipped with data-centric hardware architectures in one form or another. This shift will enable more efficient AI processing directly on edge devices, reducing energy consumption and latency while improving overall system performance

Länk till avhandlingen (på engelska, Lunds universitets forskningsportal)