Design and Synthesis of hardware based E-nose using
Sensor network and Spiking Neural Network
Praveen kumar .R1,Keerthiga.G2
1
Department of ECE, Saveetha Engineering College, Chennai.
1gkcpraveen078@gmail.com
2
Professor, Department of ECE, Saveetha Engineering College, Chennai.
2keerthiga@saveetha.ac.in
Abstract - To design a Spiking Neural Network (SNN)
chip using verilog to classify and differentiate odour in an
E-nose. Gas sensors used in electronic noses are based on
the broad selectivity profiles, mimicking the responses of
olfactory receptors in the biological olfactory system. The
process of identification of electronic nose will run into a
problem, the gas, which is detected, has the same chemical
element. Misidentification due to similarity of chemical
properties of gases is possible; it can be solved using neural
network algorithm. To avoid the misidentification the
Spiking neural network is introduced. The Spiking neural
network is design by the pitt’s model. Here the McCulloch
pitts model is considered as threshold. There are two
threshold is involving here Synaptic gap threshold and
Activation function threshold. For low power and reduced
chip size, the system is designed using Verilog.
Index Terms — Very Large Scale Integration (VLSI),
Spiking neural network, Sub-threshold oscillation.
I. INTRODUCTION
In a portable E-Nose system, learning and
classification algorithms play important roles. Since system
operation time is usually limited by the sensor responding
time rather than data recognition, the calculation speed of
the algorithm is not a vital concern, but power consumption
is substantially more critical. The biological system is
highly energy efficient, and our inspiration from it can help
us to design a low power classification algorithm.
Furthermore, the E-Nose will be composed of hundreds of
thousands of sensors in the future.
Similar to the very-large-scale integration (VLSI) system,
a biological system suffers from noise and mismatch,
however, despite this; animals can still complete their
tasks. To make the artificial system as reliable as a
biological system, many researchers have investigated how
biological systems work, and have constructed a similar
system using artificial neurons operating with action
potentials and other bio-inspired characteristics to perform
learning and classification tasks. These neural networks are
called spiking neural network. Implementing an SNN by
analog- VLSI rather than digital-VLSI may reduce the
power and silicon area of the chip. For greater mobility
and longer battery usage time, the power and the size of
an E-Nose are crucial concerns.
The proposed SNN chip may designed by Mcculloch’s
Pitts model. It is used because of sub-threshold oscillation,
it is obtained for low power consumption.
II. SPIKING NEURAL NETWORK AND SENSOR
NETWORK
To make the artificial system as reliable as a biological
system, many researchers have investigated how biological
systems work, and have constructed a similar system using
artificial neurons operating with action potentials and other
bio-inspired characteristics to perform learning and
classification tasks. These neural networks are called
Spiking Neural Network. Spiking neural networks (SNNs) fall
into the third generation of neural network models, increasing
the level of realism in a neural simulation. In addition to
neuronal and synaptic state, SNNs also incorporate the concept
of time into their operating model. The idea is that neurons in
the SNN do not fire at each propagation cycle (as it happens
with typical multi-layer perceptron networks), but rather fire
only when a membrane potential – an intrinsic quality of the
neuron related to its membrane electrical charge – reaches a
specific value. When a neuron fires, it generates a signal which
travels to other neurons which, in turn, increase or decrease
their potentials in accordance with this signal.
In the context of spiking neural networks, the current
activation level is normally considered to be the neuron's state,
with incoming spikes pushing this value higher, and then either
firing or decaying over time. Various coding methods exist for
interpreting the outgoing spike train as a real-value number,
either relying on the frequency of spikes, or the timing between
spikes, to encode information. This kind of neural network can
in principle be used for information processing applications the
same way as traditional artificial neural networks. However
due to their more realistic properties, they can also be used to
study the operation of biological neural circuits. Starting with a
hypothesis about the topology of a biological neuronal circuit
and its function, the electrophysiological recordings of this
circuit can be compared to the output of the corresponding
spiking artificial neural network simulated on computer,
determining the plausibility of the starting hypothesis.
In practice, there is a major difference between the theoretical
power of spiking neural networks and what has been
demonstrated. They have proved useful in neuroscience, but
not (yet) in engineering. Some large scale neural network
models have been designed that take advantage of the pulse
coding found in spiking neural networks, these networks
mostly rely on the principles of reservoir computing. However,
the real world application of large scale spiking neural
networks has been limited because the increased computational
costs associated with simulating realistic neural models have
not been justified by commensurate benefits in computational
power. As a result there has been little application of large
scale spiking neural networks to solve computational tasks of
the order and complexity that are commonly addressed using
rate coded (second generation) neural networks. In addition it
can be difficult to adapt second generation neural network
models into real time, spiking neural networks. It is relatively
easy to construct a spiking neural network model and observe
its dynamics. It is much harder to develop a model with stable
behavior that computes a specific function. A sensor network
(WSN)
of
spatially
distributed autonomous sensors to monitor physical
or
environmental
conditions,
such
as temperature, sound, pressure, etc. and to cooperatively pass
their data through the network to a main location. The more
modern networks are bi-directional, also enabling control of
sensor activity. The development of wireless sensor networks
was motivated by military applications such as battlefield
surveillance; today such networks are used in many industrial
and consumer applications, such as industrial process
monitoring and control, machine health monitoring, and so on.
Mcculloch’s Pitts model also known as linear threshold gate
model as a reference. This model was the earliest model ever
proposed for the function of neuron. It is a neuron of a set
of inputs I1,I2…..In and output y.
Where W1, W2……Wn are weight values normalized in the
range of either (0,1) or (-1,1).
The activation function is performed which gives the
output of the sensors. The signals generated by actual
sensor network are the action-potential spikes, and the
biological neurons are sending the signal in patterns of
spikes rather than simple absence or presence of single
spike pulse. For example, the signal could be a continuous
stream of pulses with various frequencies. With this
kind of observation, we should consider a signal to be
continuous with bounded range.
III. Mcculloch’s Pitts Model.
This model is applicable for analyzing the function of
neurons by using the more number of sensors in the E-nose
system. This model can process all the values we getting from
the sensor network and compare with the pre computed
values in the processing chip. So, we have considered
Fig 2: Sigmoid function.
Fig 1: McCulloch Pitts Model
Additionally, the sigmoid function describes the “closeness”
to the threshold point by the slope.
Input signal from sensor network is detects external
signals and passes through the network. These inputs can be
of any type ranging from pulse, square, and sine. In this
paper, I have considered two pulse inputs which are
counted and transmitted, after a certain delay. From the
McCulloch Pitts model, there are two thresholds involved:
1. Synaptic gap threshold, and
2. Activation function threshold.
Synaptic gap threshold is modeled as weight to the
AND gate, and Activation function threshold is modeled as
Fig 3: Synaptic gap threshold model.
The synaptic weight is modified according to the
STDP learning rule proposed. In a standard STDP, a presynaptic spike may cause LTD, and a post-synaptic spike
may cause LTP. The amount of weight change decayed
exponentially with respect to the timing difference
between the pre- synaptic spike and post-synaptic spike (for
LTP and inverse for LTD). However, in the STDP proposed
in, the synaptic weight was updated only when presynaptic spikes occurred, and the post-synaptic spike did
not modify synaptic weight.
Fig 5: Blocks of SNN chip
Fig 4: Activation function threshold model.
The question of whether a pre-synaptic spike causes
LTP or LTD is determined by the voltage of the soma of
the post- synaptic neuron when the pre-synaptic spike is
produced. When the soma voltage of post-synaptic
neuron is close to the threshold, a pre-synaptic spike
causes LTP, otherwise it causes LTD. To change the
synaptic weight, the pre-synaptic spike and the postsynaptic spike should occur within a specific period, which
is called the STDP window. Most of the circuits fix the
STDP window and neglect LTD when the firing rate of
the post-synaptic neuron is large enough. Instead of
wasting time to generate LTD and subsequently
neglecting it, this paper changes the STDP window
according to the firing rate of the post-synaptic neuron to
achieve a similar performance in learning with an
improved training speed. The proposed system is referred
from reference[1].
IV. EXPERIMENTAL RESULTS.
The Spiking Neural Network is designed by the McCulloch
pitts model. There are two types of threshold is involving in
this Spiking Neural Network.
1. Synaptic gap threshold.
2. Activation function threshold.
In the verilog code, the function has to be generated for these
thresholds. So the synaptic gap threshold is considered as
Adder and the Activation function threshold as Comparator.
The developed s n n c h i p also produces repeatable
responses in the measurement of three beverages using
different
sensor
batches,
hence
confirm
its
reproducibility characteristics. The developed E-nose is
also able to produce different patterns for different
samples. The patterns produced by the snn demonstrated
that the E-nose has good discriminative ability, which is
an important characteristic. Based on the results we
concluded that the developed snn is a reliable analytical
instrument for the design of E-nose.
V CONCLUSION.
In the proposed system, Spiking Neural Network is
used and simulated using verilog. SNN is designed using
Mcculloch’s pitts model. There are two threshold values
available for Mcculloch’s pitts model. The synaptic gap
threshold is designed as adder and the activation function
threshold is designed as comparator. By implementing in the
above mentioned model, totally the adders and multipliers used
in the Spiking Neural Network chip is reduced to 21 and 7. The
Spiking Neural Network is proposed in this paper is optimized
for the size power consumption. Thereby the efficiency is
improved by reducing the area, the generated look-up tables
uses 12% of the total space.
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