Page 10 - Centennial Conferences Publication 2024

Thursday, September 5, 2024 – 8:15 AM – 9:15 AM
Neuromorphic Computing Using Superconducting Electronics Ken Segall, Colgate University
ABSTRACT: The human brain is a powerful computing system, exhibiting many desired computing properties like
fault tolerance, parallelism and energy efficiency. The recent rise of artificial neural networks and deep learning, which in
turn have led to systems like Alpha Zero and Chat GPT, have been fueled by the imitation of the information-processing
mechanisms in the brain. These advances have come despite the fact that the aforementioned neural network and AI
(Artificial Intelligence) programs are typically run on conventional digital hardware, whose architecture and operating
principles are fundamentally different than the brain; this has resulted in excess power dissipation and slowdown for
these systems. Over the past decade, significant efforts have been made to produce hardware that works closer to
biological neural systems, igniting the field of neuromorphic computing. Although this field is still fairly new, neuromorphic hardware has already been successful in increasing speed and reducing power when running neural networks and AI programs.
Superconducting Electronics are a natural fit for neuromorphic computing. Many of the basic operations of neuromorphic computing like spiking and thresholding are fundamental to the physics of Josephson junctions. Low-loss superconducting transmission lines can carry pulses without distortion like dendrites and axons, and mutually-coupled superconducting loops can perform storing and weighting operations like synapses. Neuromorphic processors do not rely as heavily on dense memory circuits, typically a weakness of superconducting digital computing. Recent studies have shown that a superconducting neuromorphic processor would potentially be faster, more energy efficient and more biologically realistic than any semiconducting neuromorphic hardware available today.
In this talk the most recent and exciting developments in superconducting neuromorphic computing will be discussed. Basic neuron and synaptic circuits will be presented along with examples of spiking neural network architectures. Recent experimental results will be highlighted along with projections of future performance. Applications such as image and video processing, biological brain simulation, and fast pattern recognition will be discussed. The presentation will conclude with a possible pathway to human cortex complexity.
Friday, September 6, 2024 – 8:00 AM – 9:00 AM
What Should We Look for in New Superconductors to Make Them Useful? Alex Gurevich, Old Dominion University
ABSTRACT: Discoveries of new superconductors and advances in R&D of high-Tc cuprates and Fe-based pnictides
have shown that such captivating characteristics as high critical temperature and upper critical magnetic field are
not enough to assure applications at high magnetic fields and temperatures. Making superconductors useful involves
complex and expensive technologies addressing many conflicting physics and materials requirements which are not
only specific for a particular application but can also change, depending on the operating field and temperature. In
this talk I discuss the materials properties which would be highly desirable in new practical superconductors, and the
ways by which the performance of existing superconductors can be enhanced by tuning the materials properties and
by nano structuring. As representative examples, I consider the physics and materials science behind the optimization
of superconductors used in high dc field magnets and high-Q resonators for particle accelerators or quantum circuits. These applications have different parameters of merit and require very different ways of enhancing the material performance. Eventually, the most practical superconductors may not have the best superconducting properties but provide the best compromise between physics, materials science, technology, environmental impact, and cost.
10 CONTINUUM 2024