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A spiking neuron and 3-terminal Resistive RAM (RRAM) model are proposed and simulated as a neural network. The system is analyzed as a complex network of spiking neurons connected by synapses to demonstrate a biologically-inspired associative memory. In recent years, Machine Learning and Artificial Intelligence have become popular fields due to readily available high performance computing systems...
In this paper, a novel methodology for high-level modeling of bus communication in embedded systems is introduced. It allows the dynamic evaluation of their signal integrity (SI) characteristics at the virtual prototyping step (i.e., before physical realization). The method is based on the association of functional and nonfunctional modules. Functional modules represent the ideal behavior of the system,...
A CMOS compatible neural circuit, employing non-linear feedback, to solve a system of simultaneous linear equations is presented. The circuit has an associated energy function comprising of transcendental terms that ensure fast convergence to the solution. The use of multi-output OTAs ensures that synaptic weight resistances are eliminated thereby reducing the circuit complexity over existing schemes...
A non-linear feedback neural network for graph colouring problems is presented. The proposed circuit employs non-linear feedback, in the form of unipolar comparators realized using OTAs and diodes, to introduce transcendental terms in the energy function ensuring fast convergence to the solution. PSPICE simulation results on various random graphs have been presented.
A non-linear feedback neural network based CMOS compatible circuit to solve a system of simultaneous linear equations is presented. The circuit has an associated transcendental energy function that ensures fast convergence to the solution. The use of multi-output OTAs ensures that synaptic weight resistance are eliminated thereby reducing the circuit complexity over existing schemes. PSPICE simulation...
Spike Timing-Dependent Plasticity (STDP) is one of several plasticity rules that is believed to play an important role in learning and memory in the brain. In conventional pair-based STDP learning, synaptic weights are altered by utilizing the temporal difference between pairs of pre- and post-synaptic spikes. This learning rule, however, fails to reproduce reported experimental measurements when...
A non-linear feedback neural network for solving graph coloring problem is presented. The proposed circuit employs non-linear feedback, in the form of unipolar comparators realized using DVCCs and diodes, to introduce transcendental terms in the energy function ensuring fast convergence to the solution. PSPICE simulation results on various random graphs have been presented.
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