Nanotechnology Fabrication Commons^™

Skynet: Memristor-Based 3d Ic For Artificial Neural Networks, Sachin Bhat

Masters Theses

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 …

Reward Modulated Spike Timing Dependent Plasticity Based Learning Mechanism In Spiking Neural Networks, Shrihari Sridharan, Gopalakrishnan Srinivasan, Kaushik Roy

The Summer Undergraduate Research Fellowship (SURF) Symposium

Spiking Neural Networks (SNNs) are one of the recent advances in machine learning that aim to further emulate the computations performed in the human brain. The efficiency of such networks stems from the fact that information is encoded as spikes, which is a paradigm shift from the computing model of the traditional neural networks. Spike Timing Dependent Plasticity (STDP), wherein the synaptic weights interconnecting the neurons are modulated based on a pair of pre- and post-synaptic spikes is widely used to achieve synaptic learning. The learning mechanism is extremely sensitive to the parameters governing the neuron dynamics, the extent of …