neural cross correlation for radio astronomy chipo n ngongoni supervisor: professor j tapson...

Neural Cross Correlation For Radio Astronomy

Chipo N Ngongoni Supervisor: Professor J Tapson

Department of Electrical Engineering, University of Cape Town Rondebosch, 7701, South Africa

Chipo.Ngongoni@uct.ac.zajonathan.tapson@uct.ac.za

Neural Cross Correlation For Radio Astronomy

Outline

Neural Computation description

Outline of Research

Relevance to Radio Astronomy

Work Update

Neural Computation...• Modelling of systems according to brain response

and neural system in living organisms

• Types of models: compartmental models, rate models, spiking models

• Modeling platforms: mathematical, hardware and software

• Application areas: Wireless communications, biomedical prosthetics, pattern and speech recognition, financial analysis….

Neural Computation...

• Not all neural networks are based on training and evolving an algorithm

• J Tapson( 1998)¹ , J Tapson (2009)

• Benefits are found inherently from modelling close likeness of a biological model and extracting relevant information

J. Tapson ,1998,Autocorrelation Properties of Single neurons

J.Tapson, C.Jin et.al..2009 A First Order Non-Homogeneous Markov Model for the response of Spiking Neurons Stimulated by small phase continuous signals

Research Outline

• Neural based analysis of auto/cross correlation

• Simulate/ build a biologically inspired correlator module ( ASIC to Reconfigurable)

• Test applicability to Radio Astronomy correlation requirements

Spiking Neuron

• Basic function of spiking neuron• Integrate-and-fire model: membrane

potential• Stochastic Resonance

v t =∫m+ξ t +g t dt

drift noisesignal

Spiking Neuron

Neuron Spike Interpretation

• Wolfgang Maass: Information contained in spikes

• Spike information is contained in the spike time independent of shape and size of the spike.

• Spikes analyzed in the form ISIH and post processing logic

The Selected Model• Equivalent analog electronic circuit model • Leaky integrator which resets at hysteretic

comparator thresholds

x(t)

nx(t)

mx

y(t)

ny(t)

my

The Selected Model

• Digital analogy of the same model adopted from FPGA Based Silicon Neural Array by Andrew Cassidy et al…

• Built on Altera FPGA with VHDL and Quartus software

Digital Platforms

• 1. Digital neuron implemented on VHDL-AMS (Analog Mixed Signal).

– Ease of modelling

• 2. Field Programmable Analog Arrays:- availability

Proposed Architecture

• Signal processor CMAC in correlator

• Based on the functionalities of analog correlators and neurons

N

BU

Vxl

Vxh

x(t)

y(t) VyhVyl

Rb

Rb

Rb

RbRb Rb

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

N

BU

Proposed Architecture

clk

reset

a[7..0]

result[7..0]

clk

a[7..0]z

clk

enable

reset

q[7..0]

BUF (LCELL)

counter:Gate4

wire3

clkmain

enable

input1[7..0]

output[7..0]

comparator:Gate3basic:Gate2

Model Results

• Cross correlation

MathematicalCross Correlation

Signals

Neural Cross Correlation

Model Results

• Cross correlation

MathematicalCross Correlation

Signals

Neural Cross Correlation

Relevance to Radio Astronomy

• Neural networks not a new phenomenon to astronomy .

• Used in cluster identification, signal processing

• Spike interpretation can be analysed as bit stream correlators.

Relevance to Radio Astronomy

• Alternative technique for correlation that can switch from parallel to serial

• Cost-space allocation on FPGA

• Power Consumption

• Computation effectiveness