1. A Low Power and Area Efficient CMOS Implementation of Multilayer Feedforward Artificial Neural Network Open Access Author: Patki, Mayuresh Premanand Title: A Low Power and Area Efficient CMOS Implementation of Multilayer Feedforward Artificial Neural Network Graduate Program: Electrical Engineering Keywords: Artificial IntelligenceArtificial Neural NetworksMetal Oxide Semiconductor Implementation ServiceIntegrated CircuitCMOSVery Large Scale IntegrationMcCulloch and Pitts neuronPerceptronBackpropagation AlgorithmSynapsesGilbert Multiplier CellActivation Function CircuitFloating GateSingle Transistor Learning SynapsePost-Synaptic CurrentSpike Timing Dependent PlasticityLong Term PotentiationLong Term DepressionStatic Random Access MemoryMemristorMean Square ErrorCadence OrCAD CaptureCadence PSpice A/DElectric VLSI Design SystemNetwork Consistency CheckLayout Vs Schematic CheckXOR Classification ProblemMATLABTime DomainInstantaneous Power DissipationLoadingLearning RateMixed SignalSystem on ChipField Programmable Gate Arrays File: Download Mayuresh_Patki_Thesis_Final_Report.pdf Committee Members: Seth Wolpert, Thesis Advisor/Co-AdvisorScott Von Tonningen, Committee MemberWolfram Bettermann, Committee Member
