S. Pakin, K. Schuman, C. Teuscher. Special Section on Parallel and Distributed Computing Techniques for Non-Von Neumann Technologies, IEEE Transactions on Parallel and Distributed Systems (TPDS), 32(2), 2022, https://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=9493664
Lilak S, Woods W, Scharnhorst K, Dunham C, Teuscher C, Stieg AZ and Gimzewski JK (2021) Spoken Digit Classification by In-Materio Reservoir Computing With Neuromorphic Atomic Switch Networks. Frontiers in Nanotechnology, 3:675792. doi: 10.3389/fnano.2021.675792
Abstract: Atomic Switch Networks comprising silver iodide (AgI) junctions, a material previously unexplored as functional memristive elements within highly interconnected nanowire networks, were employed as a neuromorphic substrate for physical Reservoir Computing. This new class of ASN-based devices has been physically characterized and utilized to classify spoken digit audio data, demonstrating the utility of substrate-based device architectures where intrinsic material properties can be exploited to perform computation in-materio. This work demonstrates high accuracy in the classification of temporally analyzed Free-Spoken Digit Data These results expand upon the class of viable memristive materials available for the production of functional nanowire networks and bolster the utility of ASN-based devices as unique hardware platforms for neuromorphic computing applications involving memory, adaptation and learning.
Paper: https://doi.org/10.1145/3457388.3458673
Abstract: There is no doubt that the body of knowledge spanned by the computing disciplines has gone through an unprecedented expansion, both in depth and breadth, over the last century. In this position paper, we argue that this expansion has led to a crisis in computing education: quite literally the vast majority of the topics of interest of this conference are not taught at the undergraduate level and most graduate courses will only scratch the surface of a few selected topics. But alas, industry is increasingly expecting students to be familiar with emerging topics, such as neuromorphic, probabilistic, and quantum computing, AI, and deep learning. We provide evidence for the rapid growth of emerging topics, highlight the decline of traditional areas, muse about the failure of higher education to adapt quickly, and delineate possible ways to avert the crisis by looking at how the field of physics dealt with significant expansions over the last centuries.
Presentation: https://youtu.be/gjw9dRWaeNM
Citation: C. Teuscher, “A golden age for computing frontiers, a dark age for computing education?” In Proceedings of the 18th ACM International Conference on Computing Frontiers (CF ’21). Association for Computing Machinery, New York, NY, USA, 140–143, 2021. DOI: https://doi.org/10.1145/3457388.3458673
Paper acceptance rate: 25%
D. J. Tran and C. Teuscher. “Computational Capacity of Complex Memcapacitive Networks,” Journal of Emerging Technologies in Computing Systems, 17:2(17), 2021, doi: https://doi.org/10.1145/3445795