Time: Thursday, October 11th, 11:10
Speaker: George BOURIANOFF, Intel Corporation – retired
Artificial Intelligence (AI) related hardware and software products are projected to be the fastest growing segment of the semiconductor and microelectronic industries with projected compound annual growth rates exceeding 100% per year for the next 10 years. Reservoir Computing (RC) is one promising computational approach that enables use of naturally occurring dynamic systems for AI applications. It is a type of recursive neural network commonly used for recognizing and predicting spatial-temporal events relying on a complex hierarchy of nested feedback loops to generate a memory functionality. The RC paradigm does not require any knowledge of the reservoir topology or node weights and can therefore utilize naturally existing networks formed by a wide variety of physical processes. Most efforts prior to this have focused on utilizing memristor or optical techniques to implement recursive neural networks. This presentation examines the potential of magnetic skyrmion fabrics and the complex current patterns which form in them as an attractive physical instantiation for Reservoir Computing. We present new results showing that application of 100mV, 1GHz pulse trains of either square pulse or sinusoidal pulses will generate a strong dynamic response of the skyrmion fabric of approximately 4%. The response is observed through a dynamically varying magnetoresistance with similar time dependence strongly suggesting that the applied signal induces a magneto-dynamic response in the fabric. We hypothesize that the strong magneto-resistive response provides the basis for full RC functionality.