Design and Implementation of Neural Network Based circuits for VLSI testing By K.P. Sridhar, B. Vignesh, S. Saravanan, M. Lavanya and V. Vaithiyanathan form School of Computing, SASTRA University, India. In 2014, World Applied Sciences Journal 29 (Data Mining and Soft Computing Techniques). Presented by Name: Omar Faruq ID:12131103065 Intake: 22 nd & Sec.:02 Email: [email protected]
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Design and implementation of neural network based circuits for vlsi testing
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Design and Implementation of Neural Network Based circuits for VLSI testingBy K.P. Sridhar, B. Vignesh, S. Saravanan, M. Lavanya and V. Vaithiyanathan form School of Computing, SASTRA University, India. In 2014, World Applied Sciences Journal 29 (Data Mining and Soft Computing Techniques).
▪ Artificial Neural Network▪ Model of ANN▪ Transfer Function of ANN model ▪ Roposed Method▪ ISCAS85 Combinational Benchmark circuit▪ RESULTS AND DISCUSSION▪ Conclusion
What is ANN?▪ A neural network can be defined as a model of reasoning based on the
human brain.
▪ The brain consists of a densely interconnected set of nerve cells (information processing units) called neurons.
▪ Human brain has 10 billion neurons and 60 trillion connections.
▪ Application of ANN
▪ Robotics
▪ Traveling Saleman's Problem ▪ Data processing, including filtering, clustering.
Analogy
Inputs represent synapses
Weights represent the strengths of synaptic links
Wire presents dendrite secretion
Summation block represents the addition of the secretions
Output represents axon voltage
Model of ANN
w1
w2
wn
Y...
x1
x2
x3
Input signalsWeight Output Signals
Transfer Function
Transfer Function of ANN model
▪ Three transfer function in ANN ▪ Step Function ▪ Ramp Function ▪ Sigmoid Function
Backpropagation algorithm
is the squared error, is the squared error, is the squared error,
Sigmoid function & differentiable
Modell Of ANN
▪ Neuron will activated then when it has output logic is ‘1’ and in other remaining cases it tends to ‘0’.
▪ Dendrite of neuron is considered as fan-in for node.▪ axon is considered as fan-out.▪ Node will burst firing patterns and reproduces spiking
pulses in ANN based combinational circuit.▪ Proposed neuron method is targeted to ISCAS85-C17
benchmark circuit.
Model Of ANN
▪ Fan-Out: Different neuron sharing one source neuron which means that a net propagates a signal from one source to “n” destinations.
▪ Fan-In: The Number of input to the gate.
N
M
Roposed Method
Input Unit1
Input Unit2
Firing
Firing
Control Unit
Output Unit
In1
W1
W2
In2 CLK
Roposed Method Continue
▪ Proposed design is divided into four major units such as– Input unit– Firing Unit– Control Unit– Output Unit
▪ There are IN1 & IN2 is referred as input and W1 & W2 referred to as dendrites from others neurons along with relation weight and finally a clock signal.
▪ Output Unit : When the output would be fired, the output will have to generate a pulse to propagate it to other neuron through the axon.
▪ Firing Unit: This unit is determinate by the expositional function as f (x) = x × e-x, where x is known as input.
▪ Control Unit-Inputs IN1 and IN2 were summed together and as a result, pulse is generated.
▪ Input Unit-Initial unit consists of input IN1 and IN2 with control clock. Timer concept is also used in this unit.
Roposed Method Continue
Benchmark circuit
▪ Some Benchmark circuits are:-▪ ISCA is short form of “International Symposium on
▪ The aim of the proposed method is to have the possibility of interconnect more number of artificial neurons to create a complete neuronal network in VLSI design testing.