Commit 7a199903 authored by Sabyasachi Mondal's avatar Sabyasachi Mondal
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# FPGA in Image Feature detection and comparison with CPU
#### Group4: Sabyasachi Mondal , Ravi Yadav
fpga for streamlining of computation intensive tasks. In this case we take an hyperspectral image which is generally analysed by satellites or drones mostly consisting of single band image data. This can be used for both maritime and vehicular navigation.
#### I Have tried to build a implementation from scratch where all code structure were created so we can have much higher control over design
| Title | Content |
| ------ | ------ |
| Overview | Why do we use FPGA |
| Problem background | Where can we apply our methods and get results |
| Objective | Our vision of the solution |
| Implementation | The Hardware architechture and HLS code constructs |
| Robert's cross | The algorithm we implemented |
| What we achieved and Caveats | Objectives sucessful and limitations |
| Future Scope | Possible use in multiagent robot control using continous streaming |
fpga for streamlining of computation intensive tasks. Like in cases where signals or images from sensors are procesed continously.
### I Have tried to build a implementation from scratch where all code structure were created so we can have much higher control over design. It was sucessful and for very large images and continous streams FPGA performance shoots up as much as 3 times.
|Index| Title | Content |
|1| ------ | ------ |
|2| Overview | Why do we use FPGA |
|3| Problem background | Where can we apply our methods and get results |
|4| Objective | Our vision of the solution |
|5| Implementation | The Hardware architechture and HLS code constructs |
|6| Robert's cross | The algorithm we implemented |
|7| What we achieved and Caveats | (RESULTS) Objectives sucessful and limitations |
|8| Future Scope | Possible use in multiagent robot control using continous streaming |
|9| Tasks , Errors , References | Epilogue |
## Overview
We want to use FPGA for implementing an algorithm in hardware to perform computation more effeciently. CPU hardware is non-flexible so the code runs using the same set of registers and ALU , we cant optimize the harware as per our code. Our objective here is to harware a processing unit (something smilar to a flexible ALU using the CLBs) in the FPGA using High level code.
## Problem background
<b>*For applications like real time image processing, using CPU resource can be expensive, and the reaction time may increase in applications where decisions are based on calculations. We need a dedicated hardware that can process continous process of data coming in from sensors or camera endlessly.*</b>
<b>*For applications like real time image processing, using CPU resource can be expensive. The reaction time may increase in applications where decisions are based on calculations (see ). We need a dedicated hardware that can process continous process of data coming in from sensors or camera endlessly.*</b>
<b>*FPGA should be able to process multiple streams in synchronized manner. We want to process the streams coming from an image and process them through a convolution algorithm (Robert's matrix) and then use another function to filter out relevant parts*</b>
......@@ -81,12 +83,17 @@ We use two blocks to process the streams but that doesnot mean we use one thread
<b>*Our main goal is to ensure such a architechture runs faster in FPGA and it was reasonably fast; most importantly it can be scaled up to handle multiple streams.*</b>
#### An Important observation was that for very large images (greater then 1 MB) our resizer is almost 3 times faster, thoguh its about 0.2 times fast for lower size images.
<b>*CPU Average for images was at about 25s and FPGA at about 10s for 6 images*</b>
<b>*CPU Average for images was at about 25s and FPGA at about 10s for 6 images for smaller images*</b>
![Speed comparison in single images](https://mygit.th-deg.de/sm11312/fpga_final_project/-/raw/main/Speed_table.JPG "Speed comparison in single images")
But the best part of the result is summarized in the following:
#### An Important observation was that for very large images (greater then 1 MB) our resizer is almost 3 times faster, thoguh its about 0.2 times fast for lower size images.
Note the image below (2,3 are the smaller images) :
<img 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
">
## Future scope
*This is a new idea and has no previous references except implementaton guides.*
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