1 research outputs found
An Investigation into Neuromorphic ICs using Memristor-CMOS Hybrid Circuits
The memristance of a memristor depends on the amount of charge flowing
through it and when current stops flowing through it, it remembers the state.
Thus, memristors are extremely suited for implementation of memory units.
Memristors find great application in neuromorphic circuits as it is possible to
couple memory and processing, compared to traditional Von-Neumann digital
architectures where memory and processing are separate. Neural networks have a
layered structure where information passes from one layer to another and each
of these layers have the possibility of a high degree of parallelism.
CMOS-Memristor based neural network accelerators provide a method of speeding
up neural networks by making use of this parallelism and analog computation. In
this project we have conducted an initial investigation into the current state
of the art implementation of memristor based programming circuits. Various
memristor programming circuits and basic neuromorphic circuits have been
simulated. The next phase of our project revolved around designing basic
building blocks which can be used to design neural networks. A memristor bridge
based synaptic weighting block, a operational transconductor based summing
block were initially designed. We then designed activation function blocks
which are used to introduce controlled non-linearity. Blocks for a basic
rectified linear unit and a novel implementation for tan-hyperbolic function
have been proposed. An artificial neural network has been designed using these
blocks to validate and test their performance. We have also used these
fundamental blocks to design basic layers of Convolutional Neural Networks.
Convolutional Neural Networks are heavily used in image processing
applications. The core convolutional block has been designed and it has been
used as an image processing kernel to test its performance.Comment: Bachelor's thesi