Our member Saad Saleh successfully defended his Ph.D. thesis on Jan 7. Congratulations Dr. Saad Saleh!

Check out the CogniGron's news article featuring Saad Saleh by clicking here.

The complete thesis of Saad Saleh is available online at the following link:

1. Methods and Design for Analog Computing Architectures: Memristors for Enhancing Expressiveness and Energy-Efficiency of Packet Processors (Saad Saleh), University of Groningen, 2025.

   Abstract

   The Internet heavily relies on packet processors for establishing the communication links between senders and receivers of network traffic. Despite their promising performance, the current generation of packet processors consume a huge amount of energy and provide limited support for more expressive functions like brain-inspired cognitive computing models. The major reason is the underlying transistor-based technology, which builds on digital computations and requires energy intensive data movements between storage and computational units inside these components. In this research, we show that the recent emerging technologies from the analog domain, especially Memristors, have a huge potential for increasing energy efficiency and supporting cognitive functions in packet processors.This thesis proposes an analog architecture for packet processors in order to support energy-efficient and cognitive functions in computer networks. Central to this design is a novel analog memory abstraction called Probabilistic Content Addressable Memory (pCAM). It provides both digital and analog outputs for supporting more expressive functions in packet processors. In order to support traditional digital operations, we propose a novel memristor-based TCAM memory. Building on analog computations, we further propose a congestion control mechanism, called derivative-based active queue management (dAQM), for better management of network traffic. The performance of the proposed analog architecture was analyzed over a physically fabricated Nb doped SrTiO3 memristor chip. The results showed that analog processing consumes up to 50 times less energy than digital processing. Moreover, the analog dAQM function provides up to 39.7% better performance than the state-of-the-art algorithms.

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   BibTeX

   
   @PhdThesis{Saleh25PhDdissertation, author = {Saad Saleh}, school = {University of Groningen}, title = {{Methods and Design for Analog Computing Architectures: Memristors for Enhancing Expressiveness and Energy-Efficiency of Packet Processors}}, year = {2025}, abstract = {The Internet heavily relies on packet processors for establishing the communication links between senders and receivers of network traffic. Despite their promising performance, the current generation of packet processors consume a huge amount of energy and provide limited support for more expressive functions like brain-inspired cognitive computing models. The major reason is the underlying transistor-based technology, which builds on digital computations and requires energy intensive data movements between storage and computational units inside these components. In this research, we show that the recent emerging technologies from the analog domain, especially Memristors, have a huge potential for increasing energy efficiency and supporting cognitive functions in packet processors.This thesis proposes an analog architecture for packet processors in order to support energy-efficient and cognitive functions in computer networks. Central to this design is a novel analog memory abstraction called Probabilistic Content Addressable Memory (pCAM). It provides both digital and analog outputs for supporting more expressive functions in packet processors. In order to support traditional digital operations, we propose a novel memristor-based TCAM memory. Building on analog computations, we further propose a congestion control mechanism, called derivative-based active queue management (dAQM), for better management of network traffic. The performance of the proposed analog architecture was analyzed over a physically fabricated Nb doped SrTiO3 memristor chip. The results showed that analog processing consumes up to 50 times less energy than digital processing. Moreover, the analog dAQM function provides up to 39.7% better performance than the state-of-the-art algorithms.}, doi = {10.33612/diss.1157857827}, language = {English}, pages = {212}, publisher = {University of Groningen}, }

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