Presentation Type
Poster
Start Date
5-4-2022 11:00 AM
End Date
5-4-2022 1:00 PM
Subjects
Memristors, Memcapacitors, Neuromorphic computing
Advisor
Dr. Christof Teuscher
Student Level
Masters
Abstract
Data-intensive computing operations, such as training neural networks, are essential but energy-intensive. Memcapacitance and memristance,which can be described as capacitance and resistance, with “memory”, are properties of semiconductor devices that are observed on the nano-scale. These properties allow for data storage without a constant source of power, leading to hardware which is more energy efficient.
We intend to demonstrate that we can build specialized hardware onto which a neural network can be directly mapped using memristors and memcapacitors, improving the energy efficiency of the network. We will use Simulation Program with Integrated Circuit Emphasis (SPICE) to model our memcapacitor and memristor. Using this model, we will create a reservoir of memristive and memcapacitive elements and evaluate our design across a range of memcapacitors to memristor ratios, while testing reservoir structures including small-world, crossbar, random, hierarchical, and power-law implementations. We hypothesize that our design will greatly improve the energy efficiency and performance of neural networks.
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Persistent Identifier
https://archives.pdx.edu/ds/psu/37476
Included in
Memristors, Memcapacitors and Their Application in Neuromorphic Computing
Data-intensive computing operations, such as training neural networks, are essential but energy-intensive. Memcapacitance and memristance,which can be described as capacitance and resistance, with “memory”, are properties of semiconductor devices that are observed on the nano-scale. These properties allow for data storage without a constant source of power, leading to hardware which is more energy efficient.
We intend to demonstrate that we can build specialized hardware onto which a neural network can be directly mapped using memristors and memcapacitors, improving the energy efficiency of the network. We will use Simulation Program with Integrated Circuit Emphasis (SPICE) to model our memcapacitor and memristor. Using this model, we will create a reservoir of memristive and memcapacitive elements and evaluate our design across a range of memcapacitors to memristor ratios, while testing reservoir structures including small-world, crossbar, random, hierarchical, and power-law implementations. We hypothesize that our design will greatly improve the energy efficiency and performance of neural networks.