Omer Qadir

I am an engineer at Nordic Semiconductor where I'm part of the System Integration Group and help put together our "latest and greatest" radio ICs. On occasion, I act as a sensor (or external examiner) for NTNU.

I completed my PhD in Electronics from the University of York in 2011, and successfully fooled the powers that be into giving me the Kathleen Mary Stott Memorial prize for excellence in research. I was a member of the Intelligent Systems Group at the Department of Electronics. I was also an affiliate member of the Non-standard Computation Group of the department of Computer Science.

My PhD was part of an EPSRC funded project entitled: SABRE - Self-healing Cellular Architectures for Biologically-inspired Highly Reliable Electronic Systems. In particular it was an attempt to explore possible hardware architectures for self-organising associative memories that can display the robustness and dynamic plasticity that can be observed typically in human brains. Be sure to see the Blog page for links to the code for experiments/simulations. The following is a brief summary of the work:

The evolution of Artificial Intelligence has passed through many phases over the years, going from rigorous mathematical grounding to more intuitive bio-inspired approaches. However, to date, it has failed to pass the Turing test, which (despite some promising recent advances) is still the most well accepted test for AI. A popular school of thought is that stagnation in the 1970s and 1980s was primarily due to insufficient hardware resources. However, if this had been the only reason, recent history should have seen AI advancing in leaps and bounds - something that is conspicuously absent. Despite the abundance of AI algorithms and machine learning techniques, the state of the art still fails to capture the rich analytical properties of biological beings or their robustness. Moreover, recent research in neuroscience points to a radically different approach to cognition, with distributed divergent connections rather than convergent ones. This leads one to question the entire approach that is prevalent in the discipline of AI today, so that a re-evaluation of the basic fabric of computation may be in order.

In practice, the traditional solution for solving difficult AI problems has always been to \emph{throw} more hardware at it. Today, that means more parallel cores. Although there are a few parallel hardware architectures that are novel, most parallel architectures - and especially the successful ones - simply combine Von Neumann style processors to make a multi-processor environment. The drawbacks of the Von Neumann architecture are widely published in literature. Regardless, even though the novel architectures may not implement non-Von-Neumann style cores, computation is still based on arithmetic and logic units (ALU). My research interests are basically focused around an exploration of the possibility of whether an alternative hardware architecture inspired from the biological world, and entirely different from traditional processing, may be better suited for implementing intelligent behaviour while also exhibiting robustness. Other interests are System on Chip, Embedded Systems, Robotics and game design.