Improvement of Feedforward Mechanisms in
CMOS Analog Multilayer
Perceptron
Arjuna Marzuki
School of Science and Technology
Wawasan Open University
wou.edu.my
Introduction
●
The neurons are the most basic information processing cells of human
brain.
wou.edu.
Artificial Neural Network (Feed-forward
Network)
Single Layer
Multiple layer
wou.edu.
Edge Processing
wou.edu.
Perceptron: Artificial neuron
1. The connecting links referred
as synapses that are
characterized by a weight or
strength of its own. A signal xj at
the input of synapse j
connected to the neuron k is
multiplied by the synaptic
weight Wkj
2. An adder for summing all the
input signals, weighted by the
respective synapses of the
neuron.
3. An activation function to limit
the amplitude of the neuron
output. It is used for mapping
the inputs and the outputs.
Neural
wou.edu.
perceptron model of neuron using
analog components
wou.edu.
Multiplier
Current
Source
Input
Voltage
wou.edu.
Activation Function Circuit
wou.edu.
Conclusion-Improvement
1. One of the fundamental limits of the MLP feed-forward mechanism is input voltages. Thus, it is important
to be evaluated an improved in terms of its dynamic range as it will also determine the dynamic range of
the output.
2. the analog MLP parameters such as the weight and bias that control the feed-forward mechanisms.
Fundamentally, these parameters are the major obstacles to getting the optimum performance of the
MLP, whether the power or efficiency.
wou.edu.