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Modeling biological behaviour requires a multidisciplinary approach and very large computational resources. In this paper, we present an overview of our research work with regards to biological neuron network (NN) modeling, electrochemical reaction emulation for neurotransmitters and finite element modeling (FEM) of a very low pressure micropump for neurotransmitter concentration modulations. Three...
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, we illustrate, through examples, a novel graph-based modeling technique of two-state (on-off) PWM power converters. Differential equations of power converters are derived by inspection, based on two incident matrices, β(u) and J(u). We associate to each circuit (on, u = 1, or off, u = 0, circuits) a digraph and identify current loops (inductor-capacitor, voltage source-inductor, current...
Simulation of complex hardware circuits is the basis for many EDA tasks and is commonly used at various phases of the design flow. State-of-the-art simulation tools are based upon discrete event simulation algorithms and are highly optimized and mature. Symbolic simulation may also be implemented using a discrete event approach, or other approaches based on extracted functional models. The common...
A formal modeling and verification methodology for Pre-Charge Half Buffer (PCHB) gates and circuits is presented. PCHB gates have hysteresis and incorporate a handshaking protocol. Thus, we model gates as transition systems and provide correctness property templates that capture safety and liveness. The methodology is demonstrated using several circuits.
In this paper, we present artificial neural network (ANN) models to predict hard and soft-responses of three configurations of arbiter based physical unclonable functions (PUFs): standard, feed-forward (FF) and modified feed-forward (MFF). The models are trained using data extracted from 32-stage arbiter PUF circuits fabricated using IBM 32 nm HKMG process. The contributions of this paper are two-fold...
A robust calibration and supervised machine learning reliability framework has been developed to aid the circuit designer in the design and implementation of reliable digitally-reconfigurable self-healing RFICs. For calibration algorithm performance and reliability validation, we advocate the use of surrogate modeling, a supervised machine learning technique, which offers a significant reduction in...
We present a drift-diffusion and Poisson solver using a finite-element method to study carrier dynamics under ultra-high solar concentration. By modeling the carrier densities and the electric potential in quasi steady-state and dynamic conditions, we can use the splitting of the quasi-Fermi levels to model electrical properties such as open-circuit voltage. In this work, we analyze the validity of...
Recurrent neural networks with various types of hidden units have been used to solve a diverse range of problems involving sequence data. Two of the most recent proposals, gated recurrent units (GRU) and minimal gated units (MGU), have shown comparable promising results on example public datasets. In this paper, we introduce three model variants of the minimal gated unit which further simplify that...
The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNNs) by retaining the structure and systematically reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense...
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