The document describes three use cases implemented on the Tulipp embedded platform:
1) Pedestrian detection for ADAS achieving 15 frames/s with 2-3 frame latency.
2) Stereo depth estimation for UAVs performing in real-time with streaming optimization.
3) Medical image processing reducing radiation dose by 4x and enhancing images at 30 frames/s.
The Tulipp tools and SDSoc/Vivado HLS were used to implement algorithms from C/C++ onto the FPGA-based platform while addressing challenges of memory, streaming, and hardware-suitability. Overall, the Tulipp platform performed well across different applications with high-level development.
This presentation gives an overview of the rendering techniques used in KILLZONE 2. We put the main focus on the lighting and shadowing techniques of our deferred shading engine and how we made them play nicely with anti-aliasing.
Practical Occlusion Culling in Killzone 3Guerrilla
Killzone 3 features complex occluded environments. To cull non-visible geometry early in the frame, the game uses PlayStation 3 SPUs to rasterize a conservative depth buffer and perform fast synchronous occlusion queries against it. This talk presents an overview of the approach and key lessons learned during its development.
Rendering Technologies from Crysis 3 (GDC 2013)Tiago Sousa
This talk covers changes in CryENGINE 3 technology during 2012, with DX11 related topics such as moving to deferred rendering while maintaining backward compatibility on a multiplatform engine, massive vegetation rendering, MSAA support and how to deal with its common visual artifacts, among other topics.
This presentation gives an overview of the rendering techniques used in KILLZONE 2. We put the main focus on the lighting and shadowing techniques of our deferred shading engine and how we made them play nicely with anti-aliasing.
Practical Occlusion Culling in Killzone 3Guerrilla
Killzone 3 features complex occluded environments. To cull non-visible geometry early in the frame, the game uses PlayStation 3 SPUs to rasterize a conservative depth buffer and perform fast synchronous occlusion queries against it. This talk presents an overview of the approach and key lessons learned during its development.
Rendering Technologies from Crysis 3 (GDC 2013)Tiago Sousa
This talk covers changes in CryENGINE 3 technology during 2012, with DX11 related topics such as moving to deferred rendering while maintaining backward compatibility on a multiplatform engine, massive vegetation rendering, MSAA support and how to deal with its common visual artifacts, among other topics.
Efficient architecture to condensate visual information driven by attention ...Sara Granados Cabeza
This are the slides from my PhD dissertation. I developed a new representation map for visual information (such as disparity, optical flow, etc.) that I've called "semidense" representation. This novel representation reduces the memory and bandwidth needs for embedded platforms and real-time systems.
Linköping University has several student kitchens all over its campuses where students are given a possibility to warm their food. Critics claim that there are too few student kitchens and that the existing ones are usually overcrowded. That all kitchens are overcrowded at the same time has not been confirmed by sample inspections. One standing hypothesis is that students do not know where all the kitchens are, nor do they want to risk going to a kitchen in another building in case it is full as well.
The aim of this project is to develop a system that will provide the students with information regarding student kitchen usage. The system uses an computer vision approach, estimating the number of people currently using the kitchens. The system was developed using C++, the OpenCV library and the Qt5 library.
https://github.com/GroupDenseKitchen/KitchenOccupation
Application of the Actor Model to Large Scale NDE Data AnalysisChrisCoughlin9
The Actor model of concurrent computation discretizes a problem into a series of independent units or actors that interact only through the exchange of messages. Without direct coupling between individual components, an Actor-based system is inherently concurrent and fault-tolerant. These traits lend themselves to so-called “Big Data” applications in which the volume of data to analyze requires a distributed multi-system design. For a practical demonstration of the Actor computational model, a system was developed to assist with the automated analysis of Nondestructive Evaluation (NDE) datasets using the open source Myriad Data Reduction Framework. A machine learning model trained to detect damage in two-dimensional slices of C-Scan data was deployed in a streaming data processing pipeline. To demonstrate the flexibility of the Actor model, the pipeline was deployed on a local system and re-deployed as a distributed system without recompiling, reconfiguring, or restarting the running application.
Online video object segmentation via convolutional trident networkNAVER Engineering
발표자: 장원동 (고려대 박사과정)
발표일: 2017.8.
개요:
A semi-supervised online video object segmentation algorithm, which accepts user annotations about a target object at the first frame, will be presented. It propagates the segmentation labels at the previous frame to the current frame using optical flow vectors.
However, the propagation is error-prone. Therefore, I’ve developed the convolutional trident network, which has three decoding branches: separative, definite foreground, and definite background decoders.
Then, the algorithm performs Markov random field optimization based on outputs of the three decoders.
These process is sequentially carried out from the second to the last frames to extract a segment track of the target object.
Experimental results will demonstrate that this algorithm significantly outperforms the state-of-the-art conventional algorithms on the DAVIS benchmark dataset.
A low power and high performance software approach to Artificial Intelligence on-board.
https://klepsydra.com/klepsydra-ai-technology-evaluation-space-use/
A framework for low communication approaches for large scale 3D convolutionCarlos Reaño González
Paper presented at the 2nd International Workshop on Deployment and Use of Accelerators (DUAC). Co-located with the 51st International Conference on Parallel Processing (ICPP). August 29, 2021 (virtual event). More information at: https://duac2022.wordpress.com/
Approximation techniques used for general purpose algorithmsSabidur Rahman
Survey on approximation techniques used for general purpose algorithms, data parallel applications ans solid-state memories. It is interesting to see how approximation algorithms can contribute to solve real-life problems with better efficiency and lower cost!
Questions? krahman@ucdavis.edu.
QUIN 4.0 - Smart Drone - Final PresentationAli Ghani Syed
A presentation detailing our senior design project the QUIN 4.0 smart drone. The long nights and hard work paid off when the project won 2nd place, beating 11 other projects, for the best senior design award.
Exploration of U-Net and Support Vector Machine classification methods for UAV multispectral image segmentation
Recently, many solutions have been introduced to accurately and automatically analyze data acquired with Unmanned Aerial Vehicles (UAVs), in particular by relying on algorithms based on Artificial Intelligence (AI) techniques. Among these, the most popular are those belonging to the category of neural networks. These techniques allow the development of ad-hoc and end-to-end solutions for the classification and segmentation of different object categories through the analysis of high-resolution multispectral images. In our research, two main methodologies have been explored for the automatic segmentation of crop rows from multispectral images acquired with UAVs. The first is based on Support Vector Machines, know to handle well overfitting issues, and the other through the implementation of “U-Net”, a state-of-the-art Convolution Neural Network
The most important part of a modern PostFX pipeline is picking the right color model to support. This way the whole PostFX pipeline can use 32-bit render targets and at the same time have increased color representation and luminance representation.
Efficient architecture to condensate visual information driven by attention ...Sara Granados Cabeza
This are the slides from my PhD dissertation. I developed a new representation map for visual information (such as disparity, optical flow, etc.) that I've called "semidense" representation. This novel representation reduces the memory and bandwidth needs for embedded platforms and real-time systems.
Linköping University has several student kitchens all over its campuses where students are given a possibility to warm their food. Critics claim that there are too few student kitchens and that the existing ones are usually overcrowded. That all kitchens are overcrowded at the same time has not been confirmed by sample inspections. One standing hypothesis is that students do not know where all the kitchens are, nor do they want to risk going to a kitchen in another building in case it is full as well.
The aim of this project is to develop a system that will provide the students with information regarding student kitchen usage. The system uses an computer vision approach, estimating the number of people currently using the kitchens. The system was developed using C++, the OpenCV library and the Qt5 library.
https://github.com/GroupDenseKitchen/KitchenOccupation
Application of the Actor Model to Large Scale NDE Data AnalysisChrisCoughlin9
The Actor model of concurrent computation discretizes a problem into a series of independent units or actors that interact only through the exchange of messages. Without direct coupling between individual components, an Actor-based system is inherently concurrent and fault-tolerant. These traits lend themselves to so-called “Big Data” applications in which the volume of data to analyze requires a distributed multi-system design. For a practical demonstration of the Actor computational model, a system was developed to assist with the automated analysis of Nondestructive Evaluation (NDE) datasets using the open source Myriad Data Reduction Framework. A machine learning model trained to detect damage in two-dimensional slices of C-Scan data was deployed in a streaming data processing pipeline. To demonstrate the flexibility of the Actor model, the pipeline was deployed on a local system and re-deployed as a distributed system without recompiling, reconfiguring, or restarting the running application.
Online video object segmentation via convolutional trident networkNAVER Engineering
발표자: 장원동 (고려대 박사과정)
발표일: 2017.8.
개요:
A semi-supervised online video object segmentation algorithm, which accepts user annotations about a target object at the first frame, will be presented. It propagates the segmentation labels at the previous frame to the current frame using optical flow vectors.
However, the propagation is error-prone. Therefore, I’ve developed the convolutional trident network, which has three decoding branches: separative, definite foreground, and definite background decoders.
Then, the algorithm performs Markov random field optimization based on outputs of the three decoders.
These process is sequentially carried out from the second to the last frames to extract a segment track of the target object.
Experimental results will demonstrate that this algorithm significantly outperforms the state-of-the-art conventional algorithms on the DAVIS benchmark dataset.
A low power and high performance software approach to Artificial Intelligence on-board.
https://klepsydra.com/klepsydra-ai-technology-evaluation-space-use/
A framework for low communication approaches for large scale 3D convolutionCarlos Reaño González
Paper presented at the 2nd International Workshop on Deployment and Use of Accelerators (DUAC). Co-located with the 51st International Conference on Parallel Processing (ICPP). August 29, 2021 (virtual event). More information at: https://duac2022.wordpress.com/
Approximation techniques used for general purpose algorithmsSabidur Rahman
Survey on approximation techniques used for general purpose algorithms, data parallel applications ans solid-state memories. It is interesting to see how approximation algorithms can contribute to solve real-life problems with better efficiency and lower cost!
Questions? krahman@ucdavis.edu.
QUIN 4.0 - Smart Drone - Final PresentationAli Ghani Syed
A presentation detailing our senior design project the QUIN 4.0 smart drone. The long nights and hard work paid off when the project won 2nd place, beating 11 other projects, for the best senior design award.
Exploration of U-Net and Support Vector Machine classification methods for UAV multispectral image segmentation
Recently, many solutions have been introduced to accurately and automatically analyze data acquired with Unmanned Aerial Vehicles (UAVs), in particular by relying on algorithms based on Artificial Intelligence (AI) techniques. Among these, the most popular are those belonging to the category of neural networks. These techniques allow the development of ad-hoc and end-to-end solutions for the classification and segmentation of different object categories through the analysis of high-resolution multispectral images. In our research, two main methodologies have been explored for the automatic segmentation of crop rows from multispectral images acquired with UAVs. The first is based on Support Vector Machines, know to handle well overfitting issues, and the other through the implementation of “U-Net”, a state-of-the-art Convolution Neural Network
The most important part of a modern PostFX pipeline is picking the right color model to support. This way the whole PostFX pipeline can use 32-bit render targets and at the same time have increased color representation and luminance representation.
This document describes the current progress towards defining the reference platform. The reference platform is presented in the context of the starter kit, a conceptual package consisting of the platform instance, project applications, and reference platform handbook. The aim of the starter kit is to provide engineers with a generic evaluation platform that serves as a base for productively developing low power image processing applications.
NO1 Uk Amil Baba In Lahore Kala Jadu In Lahore Best Amil In Lahore Amil In La...Amil baba
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
#vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore#blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #blackmagicforlove #blackmagicformarriage #aamilbaba #kalajadu #kalailam #taweez #wazifaexpert #jadumantar #vashikaranspecialist #astrologer #palmistry #amliyaat #taweez #manpasandshadi #horoscope #spiritual #lovelife #lovespell #marriagespell#aamilbabainpakistan #amilbabainkarachi #powerfullblackmagicspell #kalajadumantarspecialist #realamilbaba #AmilbabainPakistan #astrologerincanada #astrologerindubai #lovespellsmaster #kalajaduspecialist #lovespellsthatwork #aamilbabainlahore #Amilbabainuk #amilbabainspain #amilbabaindubai #Amilbabainnorway #amilbabainkrachi #amilbabainlahore #amilbabaingujranwalan #amilbabainislamabad
MATHEMATICS BRIDGE COURSE (TEN DAYS PLANNER) (FOR CLASS XI STUDENTS GOING TO ...PinkySharma900491
Class khatm kaam kaam karne kk kabhi uske kk innings evening karni nnod ennu Tak add djdhejs a Nissan s isme sniff kaam GCC bagg GB g ghan HD smart karmathtaa Niven ken many bhej kaam karne Nissan kaam kaam Karo kaam lal mam cell pal xoxo
2. Implementation on embedded platforms
• All use cases started with a reference implementation in a normal
server environment
• Tools used on the embedded platform:
• SDSoc
• With the Tulipp platform installed
• Vivado HLS
• Tulipp tools:
• Stehm
• Lynsyn
• Hipperos OS
3. Workflow
• Clean the code from library dependancies not available on the
embedded platform
• Make it run on the CPU side of the SOC FPGA – handle input/output,
smaller memory footprint etc
• Identify sections of the code that are candidates for HW acceleration
• Refactor/restructure the algorithm to optimize for the given
conditions:
• Streaming
• Small local memory
• Preferably no floating point
5. Viola/Jones classification
• Machine learning algorithm based on training with labeled data
• The classifier is the weighted sum of ”rectangular features”
• The weights and what features to chose is selected by the training
process
6. Rectangular features
A classifier consists of a
large number of features
calculated for a given
path.
If the sum is above a
threshold, the patch
contains a pedestrian.
A feature is the sum of all
pixels in a rectangular
region.
7. Integral images
• In an integral image, each pixel stores
the sum of all pixel to the left and
above that pixel in the original image
• With an integral image, the sum of all
pixels in an arbitrary rectangle can
easily be calculated with a small, fixed
number of operations
x, y
12. Challenges
• High memory bandwidth requirements – combined with a non-
sequential access pattern
• 30 frames/s
• 50 patch sizes
• Sweping over all image positions
• Each classifier requires roughly 1000 feature calculations
• Ineffective pipelining since the classifier calculation can terminate at
any stage
• Not all data can be kept locally (cached)
13. So we need some tricks
• Cascading – successively trained classifier chain that emphasizes on eliminating
non-pedestrians quickly. Reduces the number of classifier steps on average with
at least a factor of 10.
• The classifiers does not need to test every single position, instead scan in a grid
• Results in a need for 5-10 Gbyte/s – random access!
Patch with
possible
pedestrian
No pedestrian No pedestrian No pedestrian
Pedestrian!
…
14. Random access on DDRs is very ineffective
• The trick was to find data requests that were on the same DDR cache
lines
• That required us to rewrite the algorithm so it calculates many
classifiers at the same time
• By then reordering all accesses in a cache friendly manner, the
resulting memory bandwidth increased to almost the same as for
fully sequential accesses
15. Result
• Reference implementation on PC platform – 10 s/frame
• Final implementation on the Tulipp platform
• 15 frames/s
• Latency of 2-3 frames
17. The UAV Use Case objectives
• To perform real time stereo depth estimation
• To detect obstacles based on the depth estimation and to avoid
collision
• Based on dual cameras forming a stereo pair
• Lower weight and lower price than a depth camera
• Requires real time performance – high measurement rate and low
latency
18. StereoDepth Estimation
• Two cameras with baseline 𝑏, observe an object 𝑀 at two different locations 𝑥1 and 𝑥2
• Depth 𝑍 can be computed from disparity 𝑑 = |𝑥1 − 𝑥2| ∝
1
𝑍
• Disparity computation requires detection of same objects in both images
Stereo camera setup
19. Algorithm Description
• Stereo algorithm with Semi-Global-Matching [1] optimization
[1] H. Hirschmueller, Accurate and efficient stereo processing by semi-global matching and mutual information,
IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
Input: Stereo Images
Output: Depth map
Image Rectification /
Pre-processing
Depth Estimation
Local matching
Semi Global Matching
Left-Right
Consistency check
Median filtering
20. Semi-Global Matching
𝐸 𝐷 =
𝐩
𝐶𝑜𝑠𝑡 𝐩, 𝐷p +
𝐪∈𝑁p
𝑃1T 𝐷p − 𝐷q = 1 +
𝐪∈𝑁p
𝑃2T 𝐷p − 𝐷q > 1 , 𝑚𝑖𝑡 𝑃1 ≤ 𝑃2
Large discontinuity – large penaltySmall discontinuity – small penalty
Aggregation along paths solved using
dynamic programming
21. Stereo Depth Estimation
The depth z can be calculated as 𝑍 = 𝑓 ⋅
𝑏
𝑑
Where f is the focal length, B the distance between the cameras and d the disparity.
Input image Corresponding depth image
22. Obstacle Avoidance
Reactive obstacle avoidance algorithm computing shortest path around
obstacle based on disparity map
1. U- / V-Map computation (Oleynikova et al. 2015)
2. Binary filtering and contour detection
3. Obstacle extraction and waypoint computation
U- / V-Map Binary filtering Contour Detection
Obstacle
extraction
Waypoint
Computation
24. SGM optimization for streaming
• Original algorithm used aggregation
along 8 paths
• That requires access to the full image
• In the FPGA implementation, the full
image can’t be stored locally, hence a
streaming solution would be preferred
• By only aggregating along 4 paths,
streaming can be used.
• Only 1.7% accuracy reduction when
going from 8 to 4 paths
25. Implementation and Results
The disparity estimation is
implemented in C/C++ and
synthesized to the FPGA using
HLS
The obstacle avoidance is
purely implemented on CPU
part of the SOC FPGA
26. The medical use case
• Used on X-ray video for surgery
• Lower the radiation dose by a factor of 4
• Enhances the image quality by denoising and image filtering
• Operates on 1024x1024 24 bits images @ 30 Hz
27. Current solution vs the goal
RAW IMAGE
PC
dedicated
to Thales
Sensor
Cleaned &
Enhanced
Image
UI
Current Xray Sensor architecture
With Tulipp
- Reduce Costs
- Reduce Size
- Ease integration
- Choose a MPSoC
GigE-Vision+Msg
Nano Processing Unit
Inside the sensor
Based on SoC
(credit card size board)
Future Xray Sensor
architecture
Cleaned &
Enhanced
ImageGigE-Vision+Msg
28. Multi pass image filtering
• The image is filtered with several different methods
• Together they perform:
• Remove sensor defects
• Emphasize low contrast parts of the image
• Enhances details and edges
• Adapt the image to the final display
30. Typical processing sequence:
Clean image stage
• Remove dead pixels
• AGC – Automatic Gain
Control
• ABC – Automatic Brightness
and Contrast – feedback to x-
ray sensor
32. Typical processing sequence:
Clip & Spatial filters
• Clipping to reduce the signal
levels of the very bright areas
• Spatial filtering for smoothing
(convolution)
33. Typical processing sequence:
Multiscale contrast & edge enhancement
• Multiscale filtering using
Laplacian Gaussian pyramid
• Iteratively operates on
downscaled images in a
”pyramid”
• A low pass filterede image is
subtracted from the original
in each step, to extract the
high frequency components
• Final image is composed of
the result from each level
36. Challenges
• Handling of all scales in the pyramid filtering – requires much more
memory than locally available
• Some of the filters had to be redesign since they had too many
branches, which is poor for hardware streaming solutions
• Implemented from C/C++ using SDSoc
37. Results
• The algorithm, although slightly modified, run s on the Tulipp
platform:
• 29 frames/s
• 29 ms latency
38. Conclusion
• The three use cases show that the Tulipp platform performs well for
quite different applications
• The Tulipp tools together with the vendor tools offers a nice
development environment, where you actually can get effective FPGA
implementations using high level tools, based on C/C++
• Important to remember – a large portion of the work will (always) be
to refactor/restructure the algorithm to fit the underlaying hardware
structure
