Engineering Commons^™

Memristors, Memcapacitors And Their Application In Neuromorphic Computing, Nithyakalyani Sampath

Student Research Symposium

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 …

Growing Reservoir Networks Using The Genetic Algorithm Deep Hyperneat, Nancy L. Mackenzie

Student Research Symposium

Typical Artificial Neural Networks (ANNs) have static architectures. The number of nodes and their organization must be chosen and tuned for each task. Choosing these values, or hyperparameters, is a bit of a guessing game, and optimizing must be repeated for each task. If the model is larger than necessary, this leads to more training time and computational cost. The goal of this project is to evolve networks that grow according to the task at hand. By gradually increasing the size and complexity of the network to the extent that the task requires, we will build networks that are more …