Hardware Emulation Study of Neuronal Processing in Cortex for Pattern Recognition

Neural network (ANN) is an area of computing that is modeled after the network of the biological brain and over the last few decades, has experienced huge success in its application in areas such as business, Medicine, Industry, Automotive, Astronomy, Finance, etc.

Since Neural are inherently parallel architectures, there have been earlier researches to build custom ASIC based systems that include multiple parallel processing units.

However, these ASIC based systems suffered from several limitations such as the ability to run only algorithms and limitations on the size of a network. Recently, much work has focused on implementing artificial neural networks on reconfigurable computing platforms.

Reconfigurable computing allows to increasing the processing density beyond that provided by general-purpose computing systems.

Field Programmable Gate Arrays (FPGAs) can be used for reconfigurable computing and offer flexibility in design with performance speeds almost closer to Application Specific Integrated Circuits (ASICs).

This thesis presents a study of an FPGA-based acceleration solution and performance exploration of a Feedforward Artificial Neural Networks (FFANN).

The architecture is described using Very- High-Speed Integrated Circuits Hardware Description Language (VHDL) and implemented and demonstrated on an FPGA board.

Synthesis and simulation are made with Quartus II tool and ModelSim respectively. The given system was efficiently trained and evaluated in hardware with digit recognition application.

Introduction

Background Of Study

Moore’s law predicted that the number of transistors on a dense integrated circuit doubles every two years(Moore, 1975).

So far, this has been true, but it is only a matter of time before this circuit will max out, and this is because further increasing the number of transistors on it will make it consume more power, overheat and become impossible to cool.

Again, it is difficult to get the conventional computer with Von Neumann architecture to perform operations like understanding human languages, recognizing objects, learning to dance, etc. activities the human brain does very easily.

The human brain is not good at arithmetic operations, but it does well in operations that involves processing continuous streams of data from the environment and can do it very quickly.

So, to build a computer that will be able to carry out these activities, a computing paradigm called artificial neural network which mimics the biological brain was adopted(Abdallah, 2017).

Artificial Neural Network is a computing paradigm after the neural network of the biological brain. The Biological brain is made up of billions of neurons which are interconnected to form a network.

It is fault tolerant, consumes extremely low amount of power and can carry out significant parallel computations(Indiveri, Linares-Barranco, Legenstein, Deligeorgis, & Prodromakis, 2013).

This computing paradigm started as early as 1943(Macukow, 2016) and has continued to improve having its application in the areas of pattern recognition; a discipline that is aimed at classifying objects (text, images, speech, etc.), image recognition, object classification and much more.

References