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TLpq2404611257
انگلیسی
Hybrid Memristor-CMOS Computer for Artificial Intelligence:
[Thesis]
Lee, Seung Hwan
From Devices to Systems
Lu, Wei
University of Michigan
2020
162
Ph.D.
University of Michigan
2020
Neuromorphic computing systems, which aim to mimic the function and structure of the human brain, is a promising approach to overcome the limitations of conventional computing systems such as the von-Neumann bottleneck. Recently, memristors and memristor crossbars have been extensively studied for neuromorphic system implementations due to the ability of memristor devices to emulate biological synapses, thus providing benefits such as co-located memory/logic operations and massive parallelism. A memristor is a two-terminal device whose resistance is modulated by the history of external stimulation. The principle of the resistance modulation, or resistance switching, for a typical oxide-based memristor, is based on oxygen vacancy migration in the oxide layer through ion drift and diffusion. When applied in computing systems, the memristor is often formed in a crossbar structure and used to perform vector-matrix multiplication operations. Since the values in the matrix can be stored as the device conductance values of the crossbar array, when an input vector is applied as voltage pulses with different pulse amplitudes or different pulse widths to the rows of the crossbar, the currents or charges collected at the columns of the crossbar correspond to the resulting VMM outputs, following Ohm's law and Kirchhoff's current law. This approach makes it possible to use physics to execute direct computing of this data-intensive task, both in-memory and in parallel in a single step.
Integrated memristor/CMOS System
Memristor
Neuromorphic
Oxygen vacancy drift and diffusion
Tiled architecture
Vector matrix multiplication (VMM)
Lee, Seung Hwan
Lu, Wei
مطالعه متن کتاب p
[Thesis]
276903
a
Y
