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Electrical & Computer Engineering
Master of Science (M.S.)
Year Degree Awarded
Month Degree Awarded
Hardware implementations of artificial neural networks (ANNs) have become feasible due to the advent of persistent 2-terminal devices such as memristor, phase change memory, MTJs, etc. Hybrid memristor crossbar/CMOS systems have been studied extensively and demonstrated experimentally. In these circuits, memristors located at each cross point in a crossbar are, however, stacked on top of CMOS circuits using back end of line processing (BOEL), limiting scaling. Each neuron’s functionality is spread across layers of CMOS and memristor crossbar and thus cannot support the required connectivity to implement large-scale multi-layered ANNs.
This work proposes a new fine-grained 3D integrated circuit technology for ANNs that is one of the first IC technologies for this purpose. Synaptic weights implemented with devices are incorporated in a uniform vertical nanowire template co-locating the memory and computation requirements of ANNs within each neuron. Novel 3D routing features are used for interconnections in all three dimensions between the devices enabling high connectivity without the need for special pins or metal vias. To demonstrate the proof of concept of this fabric, classification of binary images using a perceptron-based feed forward neural network is shown. Bottom-up evaluations for the proposed fabric considering 3D implementation of fabric components reveal up to 19x density, 1.2x power benefits when compared to 16nm hybrid memristor/CMOS technology.
Csaba Andras Moritz
Bhat, Sachin, "SkyNet: Memristor-based 3D IC for Artificial Neural Networks" (2017). Masters Theses. 552.