Purpose

With the progress of artificial intelligence (AI) technology, social implementation such as image recognition using AI technology, automatic driving, and natural language processing is rapidly progressing. Especially in the edge computing field, since it is necessary to realize AI technology with higher efficiency, startups mainly in the United States and China and major vendors are accelerating their entry into AI hardware, and even in Japan Not only that, there is an urgent need to develop human resources and industries for hardware to accelerate AI processing such as LSI and FPGA.
Against this background, we will carry out this contest, in addition to the conventional AI technology development centered on software, we are aiming to develop human resources and startups with AI hardware in mind, as well as industry development utilizing these technologies. In this contest, we will develop AI hardware equipped with the "RISC-V" chip, which is currently attracting attention, and set the hardware system development that considers edge computing including hardware and software (network model and system optimization) as an issue.

AI Edge Contest Official Website

The 1st AI Edge Contest (Algorithm Contest (1))
The 2nd AI Edge Contest (Implementation Contest (1))
The 3rd AI Edge Contest (Algorithm Contest (2))
The 4th AI Edge Contest (Implementation Contest (2))

*Please refer to the winners' solutions in the past AI edge contest.

Contest Outline

Subject (Algorithm development) Create an algorithm that detects a rectangular area in which objects are captured from the videos taken by a camera forward facing a vehicle and tracks the objects.
(Algorithm implementation) Design hardware accelerators and implement algorithm on the target platform equipped with RISC-V.
Provided data (Train/Test) videos of a vehicle front camera
(Train) Rectangular tag area labeled with objects, category and object ID
Identifying target Car, Pedestrian
Implementation Using RISC-V in the processing of object tracking(For the details, please refer to the Rules)
Award Processing speed award Idea award
Platform Avnet Ultra96-V2 FPGA board<
(Zynq UltraScale+ MPSoC ZAU3EG SBVA484)
Any device
Evaluation Processing speed of the target FPGA board
RISC-V utilization ideas
Winning requirements The MOTA of the object tracking result of the test data is over 0.6, etc.
It is not difficult to apply it practically, such as the MOTA of the object tracking result of the test data is much lower than 0.6 (less than half).
Final result set Source code, area report, implementation approach, processing performance, etc.
Source code, implementation report, video of the operation, etc.
Prize. TBD (top 3 teams) TBD(Up to 3 teams)
Special prize Forum Activity Award / Article Writing Award
TBD(Up to 3 teams)
(Forum Activity Award) Those who have greatly contributed to solving the problem in the forum.
(Article Writing Award)Those who wrote articles with high educational effect on the Web on blogs etc. and published links on the forum.
* See the "Evaluation" tab for details on winning requirements.(The MOTA requirement standard values ​​are subject to change)


Subject (Algorithm development)
Allocate a rectangular area containing objects to be predicted as a bounding box = (x1, y1, x2, y2) to the video captured by the vehicle front camera, and give any unique object ID to the same object in each video. With allocations of multiple bounding boxes to each frame in the videos, the bounding box is represented by specifying four coordinates with the upper left corner as the origin (0,0), upper left coordinate of the object area (x1, y1), and the lower right coordinate (x2, y2).

However, the object to be evaluated (objects to be inferred) is limited to those that satisfy all of the following.
・There are two categories: “Car” and “Pedestrian”
・Objects with 3 or more frames in each video (Frames do not have to be continuous)
・Objects with a rectangle size of 1024 pix² or more

*Even if an object appears in three or more frames in the video, if there are less than 1024 pix² or more in three frames, the object including the size of 1024 pix² or more will not be evaluated.
*Please note that if an object that does not meet the conditions is detected, it is considered as a false detection.



Subject (Algorithm Implementation)

Implement the developed algorithm on the target platform equipped with a RISC-V chip.

The section to be optimized and the processing time to be measured is defined as follows.
Time to perform inference processing on the video data loaded in the memory and then convert it to JSON-equivalent information such as the ID and rectangle information of each object.

Carefully check the RISC-V usage requirements described in the "Rules" tab before implementing.


Data


For TrainFor Test
RouteTokyo (Shibuya – Akihabara)
Time ZoneDay time
Resolution1936 x 1216
FPS5.0
Category TypesCar, Pedestrian, Truck, Signal, Signs, Bicycle, Motorbike, Bus, Svehicle, TrainCar, Pedestrian
Number of Videos25 videos74 videos
Length of an videos120 seconds30 seconds
Number of Frames per video600150


Travel Route



Flow of entry / registration for the contest
1. Sign up for SIGNATE (Please register through the member registration button on the top right of the page if you are not currently a member.).
2. Download the data from the data tab (Please agree the terms of entry that will be displayed when downloading.).
3. Develop an algorithm and implement / optimize it on the selected target environment
   * We plan to provide FPGA board to a maximum of 50 applicants based on the review of application among the people who apply through application form for the board offer document review. (The number of boards offered may increase.)
   * If you do not have an Ultra96-V2 board and would like to use it as a target environment, please purchase it yourself or apply by clicking the button below.

4. During the contest, submit the object trakking result from the "Submit" button on the upper right of this page and check the MOTA score on the leaderboard.
   * Please refer to the MOTA function that can be downloaded on the data page.
   * The MOTA score until the last day of the contest (public score) and the MOTA score after the contest (private score) are the same.

5. (Optional) During the contest, we will submit the provisional processing time ranking as reference information, so please cooperate.
   * Please submit the average processing time [ms] per video at the time of posting and the evaluation result by MOTA from the "Submit" button on the page that transitions from the following.
   * Submits are accepted at any time until the deadline of 2022/1/15, and can be updated.(The latest results will be displayed)
   * In the case of a team, the team leader should submit it as a representative. The user name of the poster is displayed on the interim processing time leaderboard.

6. Submit the final result set by the end of the contest.
   * For details, refer to "submission_details.pdf" that can be downloaded from the data page.
   * Upload on the Internet such as Dropbox, Gigafile, etc., and inform us of the information required for downloading to the following form.





The AI ​​Edge Contest is also collaborating with the 3rd Japan Automotive AI Challenge sponsored by the Society of Automotive Engineers of Japan to promote the discovery and training of engineers.

Decision method of winners
The secretariat will confirm the submitted final result set, and from those who meet all of the following requirements, they will decide the prize winner based on the processing performance time (ms) for the processing speed award and the content of the report for the idea award. An online interview may be set when necessary, centering on the idea prize, and a presentation (assuming 5 minutes presentation + 10 minutes Q&A) will be held such as implementation ideas.

Processing speed AwardIdea AwardRequirements
You have submitted the final result set by the end of the contest, and you have all the necessary submissions.
Agree to grant an open source license to your submission.
Meet the rules.
-Operate on the Ultra96 board used in the examination (confirm the operation on multiple boards for individual differences)
-The processing performance measurement application operates and the processing performance results are displayed on the file and/or console

The output of the object tracking results (JSON file) can be confirmed, and there is no big difference between the IoU value calculated from the result and the MOTA value displayed on the leaderboard.
-The object tracking result submitted during the contest period displayed on the leaderboard exceeds the standard MOTA = 0.6.
-The object tracking result submitted during the contest period displayed on the leaderboard is far below the standard MOTA = 0.6 (less than half), and it is not difficult to apply in practice.
-The report "report.pdf" is included in the final result set.

*The standard MOTA is displayed on the leaderboard as a benchmark.

Evaluation of Recognition Accuracy
・ It is evaluated based on the evaluation function “MOTA (multiple object tracking accuracy)”
・ The maximum evaluation value is 1, and the higher the accuracy, the larger the value. It can be negative.
・ Please refer to the implementation example of the evaluation function which can be downloaded from the data page.

1. When the overlap (IoU) of the prediction area and the correct area (rectangle) exceeds 0.5 for all the frames in an video, it is determined that the area can be detected.
2. Calculate MOTA for each subjected prediction category based on the detected result.
3. Calculate the average of 2. of the all subjected prediction categories.
4. Calculate the average of 3. of the all videos for evaluation.



Reference: https://cvhci.anthropomatik.kit.edu/~stiefel/papers/ECCV2006WorkshopCameraReady.pdf


About implementation report

It is not necessary to cover everything, but if you appeal to these points, the points for judging the idea award will be high.
・Outline of implementation and demo operation
    - System configuration/architecture outline of implemented hardware in FPGA
    - Operating frequency, performance, etc.

・Content of ingenuity and appeal points (Especially from the viewpoint of RISC-V utilization)
    - Ingenuity on algorithm (network model used, learning method such as assembly, improvement of data set, etc.)
    - Architectural innovations (optimization of speed and area)
    - Electrical consideration (measurement comparison with wattmeter etc.)
    - Others (cost considerations, devices for shortening development period, etc.)

・The report itself
    - Easy to understand each issue, clarification of ingenuity, etc.

Premise of participation
・ Please understand the purpose set in the competition and build a system that is conscious of practicality. Practicality here means that it is fully automatic processing, high accuracy, high speed, low calculation amount (HW cost saving), scalable with respect to the amount of data, and high model interpretability.
・ Participation by cheating or ignoring the rules is not allowed.
・ It is prohibited to slander others or to act against public order and morals. If the secretariat determines that it is malicious, please be aware that there is a possibility of deprivation of winning qualifications and membership qualifications before participating.

Use of System
・ You may have one account per participant.
・ If you want to join as a team, you can create a team of up to five people by Jan 31, 2022. (Click here for how to create a team)

Handling of Information
・ Public disclosure of algorithms, ideas or other materials created to participate in this competition shall not be restricted, only if it is clearly stated that those are created to participate in this competition at the time of publication.
・ However, at the public disclosure, please specify it (including links) in the forum of this competition so that all participants can view it.

Use of Data
・ Use of the data other than provided data, and learned data shall not be restricted if the method allows verification of reproducibility for free. For example, the dataset with non-profit purpose, such as Waymo Open Dataset, can be used.
・ However, use of data provided by past AI Edge Contests is prohibited.
・ Modification of training data (including manual labeling and label rewriting) is allowed.
・ For API, the data that can be republished as an open source can be used.
・ Inference is performed on video-by-video basis. When tracking an object in videos, future information can be used if it is the data for test in the same video (tracking two-way). However use of the data in other videos is prohibited.

Creating algorithms
・ If you enter new data in the format provided in the competition, only the model for which the prediction result is automatically output will be evaluated.
・ The proposed method should be reproducible without any additional costs (you are not allowed to not use any paid external APIs, etc.), and the algorithms are meant to be used after this competition.

Way to implement
Use RISC-V Cores during object tracking tasks.
The cases where RISC-V is used in this contest are permitted and not permitted below.

【Permitted cases】
・ RISC-V is used as microcomputer controller during Deep Neural Network processing on FPGA.
・ Participant designed some special operator by extending RISC-V ISA to accelerate Deep Neural Network processing on FPGA
・ Vitis AI is used for object tracking, and after that, RISC-V is used in the processing on the video data loaded in the memory and then convert it to JSON-equivalent information such as the ID and rectangle information of each object
・ (For Idea award) Use the SoC K210 equipped with RISC-V, and implement object tracking process with Deep Neural Network core embedded in SoC

【NOT permitted cases】
・ Use RISC-V as result output only (not used in core processing or controller of component potion.)
・ Use a emulator of RISC-V (e.g. QEMU) on the top of ARM, and process the core part  in the emulator

The purpose of this contest is to develop human resources involved in the technology and application of the RISC-V, which is expected to be used in the Edge AI field in the future, and to educate the public on how to use RISC-V.

Judging criteria may be added in the future at the discretion of the Steering Committee or other relevant committee members at least one month before the contest deadline. If other cases arise, the review committee will deliberate on them and make a decision on an individual basis.

Host:Ministry of Economy, Trade and Industry / New Energy and Industrial Technology Development Organization (NEDO)

        

Co-host:SIGNATE Inc.
Support:IEEE Japan Office / The Society of Instrument and Control Engineers (SICE)
Sponsors:Society of Automotive Engineers of Japan / Google Japan LLC / Sony Corporation / SoftBank Corp. / Sompo Japan Nipponkoa Insurance Inc. / Digital Media Professionals Inc. / Panasonic Corporation / NSITEXE Inc. / 
Tier IV, Inc.

     

     

      

Endorsement:IEEE Japan Office / The Society of Instrument and Control Engineers / The Japanese Society for Artificial Intelligence / The Institute of Electronics, Information and Communication Engineers

Steering Committee Members

  • Shinpei Kato Tokyo University
  • Naoki Suganuma Kanazawa University
  • Hiroaki Matsumoto Sony Semiconductor Solutions Corporation
  • Eisaku Ohbuchi Sony Semiconductor Solutions Corporation
  • Hironobu Fujiyoshi Chubu University
  • Masato Motomura Tokyo Institute of Technology
  • Hiroki Nakahara Tokyo Institute of Technology
  • Yoshiki Ninomiya Nagoya University
  • Teruaki Sakata Hitachi, Ltd.
  • Kohei Ozaki Preferred Networks
  • Atsuto Suyama BOLDLY, Inc.
  • Masaki Hiraga Morpho, Inc.

Reference information for obtaining the Ultra96 board to use for this contest

・Subjected board for this contest is Ultra96-V2.
・Regarding method to obtain: There are two ways to obtain it.

1. Provided from this contest
- Although limited in number, the Ultra96 board will be provided after review for the contest participants. For those of you who wish to be provided, please apply through the "application form for board offer document review".
- Provided board includes an AC adapter, power conversion cable, and JTAG board.

2. Obtain from the Avnet website
- You can purchase from here.
- We recommend you to purchase the following related parts along the purchasing of the board.
JTAG board for Ultra96: It is necessary to connect the UART (serial) console.
・Avnet recommended AC adapter, power conversion cable
 - Power supply
 - Conversion connector

Regarding the errata of Ultra96 board
The errata from Avnet is published the following site.
http://avnet.me/Ultra96-V2_Errata

The errata particularly has the following known issues:
・Reboot issue of Ultra96-V2
 - Ultra96-V2 When the load of the PL part goes up high, MPSoC reboots.
 - Ultra96-V2 It is posted on the errata reporting page.
This is caused by the reset operation in MPSoC that was triggered by the PowerGood signal's drop when a specific threshold is exceeded while the IC called PMIC is monitoring the Vccint voltage.
- In the current V2 board, a reset is applied when a set threshold is exceeded due to increase in current consumption of the PL section.
- How to distinguish the PL power supply current setting is as follows.


・Also for boards with power limits of 3.5A and 4.0A, The limit of this errata can be relieved to about 5.5A by revising the board and changing the setting of PMIC. A complete set of countermeasure procedures prepared by Avnet can be downloaded from below.

 20191114_Ultra96-V2 PMIC update procedure.zip (This can be obtained by the data tab on this website)
 Infinion that is required for the measures of this issue.
 The sales information of USB005 writing cable is as follows.
 Mouser
 Digikey
 Chip1Stop

・We plan to provide a board that supports 5.5 A of PL power supply current for the board offered from this contest.

You will license your winning Submission and the source code used to generate the Submission under an Open Source Initiative-approved license (see www.opensource.org) that in no event limits commercial use of such code or model containing or depending on such code. As a reference, we will post a comparison table of representative open source writers.

License 
RequiredPermitted
Forbidden
Apache License 2.0 (Apache-2.0)Display copyright, Explicit change placementCommercial use, Modification, Distribution, Sub license, Patent grantUse trademark, Liability exemption
BSD License: See 3-clause BSD License and 2-clause BSD LicenseDisplay copyrightCommercial use, Modification, Distribution, Sub license
Use trademark, Liability exemption
GNU General Public License version 3 (GPL-3.0)Display copyright, Explicit change placement, Explicit sourceCommercial use, Modification, Distribution, Patent grant
Liability exemption,  Sub license
MIT License (MIT)Display copyrightCommercial use, Modification, Distribution, Sub license
Liability exemption

Disclosure policy

As a general rule, in accordance with Article 4, Paragraph 1 of the terms of participation, diclosing any contents such as insights and deliverables transmitted through the information or data provided by our company in relation to this competition is not permitted, however, only after the completion of this competition and for non-commercial purposes, it will be possible to disclose the contents within the score of the table below
Model *1
Public
Analysis results *2
Public
Public : Posting to social media sites, blogs and source repositories, and citing to papers
Restricted : Using in a limited range from research, education to seminars, where many unspecified people cannot access
*1 Execution unit source code and learned models
*2 The insights obtained using the information and data provided, or the solutions including scripts and processed data such as summary statistics

Q. There was an obvious mistake in the label of the training data. What should I do?
A. It is possible to correct the learning data according to the rules, so please delete or correct it. We check all kinds of data, but it is difficult to detect and correct 100% of mistakes, so we take the present result as "correct" and analyze it. Thank you for your understanding.


Q. What happens to the ID when the same object is actually less than 1024 pix² in size, hidden behind another object, or left off the screen and then returned? 
A. Even if they are discontinuous, if they are actually the same object, they will be given the same ID.


Q. If there are the same objects in frames 1 to 3 at 2000, 2000 and 1000 pix² respectively, will only the objects in frames 1 and 2 be judged as labels? Or, since the number of frames is less than 3 as a result of being filtered by the rectangle size condition earlier, is it not all included in the label as a result?
A. All are not included in the correct answer label because they are filtered by the condition first. Even if an object appears in three or more frames in the video, if there are less than 1024 pix² or more in three frames, the object itself, including 1024 pix² or more, will not be evaluated.


Q. It seems that the code for calculating MOTA that can be downloaded on the data page does not include filtering of the three conditions to be inferred such as category, number of frames to appear, and size of rectangle.
A. When actually using the distributed MOTA code and verifying it in a local environment, please apply the conditions to the annotation that is the true value. If an object that does not meet the conditions is detected, it will be treated as a false detection. Therefore, when creating a post file, please post by yourself with the contents excluding the objects that satisfy the conditions removed.


Q. For "objects with more than 3 frames", is the first 2 frames that appear also considered in the judgment?
A. Yes. The judgment is made from the first frame.


Q. Do "objects with more than 3 frames" need to be continuous?
A. No. It does not have to be continuous.


Q. I implemented it on an Ultra96 board. Please tell me how to make an SD card image for submission.
A. Please refer to the forum / article of the 2nd contest below for how to create an SD card image.

https://signate.jp/competitions/191/discussions/sd

▼ Special Contract on this Contest
In accordance with Article III “Prizes and Rights” of the special regulations established for this contest as described below, participants will either choose to make their own submission data, source code and other relevant items be licensed under an Open Source Initiative approved license (www.opensource.org) or not. This license imposes no restrictions on commercial use for licensed models, source code, and any results attained using said source code. Incidentally, the participants who will not choose this license will not be able to prize winner candidate, even though they can participate in the contest. In regard to posting and/or other publication on any social media site, blog or similar by the participant of information relating to any algorithm developed by said participant or any other materials produced for or related to this contest (including materials used for final submissions and/or final evaluations/inspections), no restrictions apply to posting and/or publication as long as the participant clearly explains that the posted/published items relate to this contest, and absolutely posts the positing and/or publication information (including the link URL to the pages) on the contest forum in order to let all the participants know it.


Terms of Participation in SIGNATE Competition

In order to participate in the Competition, you are required to agree to these Terms, in addition to the Terms of Use of SIGNATE.JP Site (hereinafter referred to as the “Terms of Use”). You should participate in the Competition after reading carefully and agreeing to these Terms. These Terms, the matters that are displayed as “additional matters” that you have agreed to when participating in a Competition, the Terms of Use and other terms and conditions that you have agreed to (hereinafter collectively referred to as “these Terms, etc.”) shall all be binding on the Participant.


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3. Any Participant may publish any algorithms that they have developed as well as any other materials they have created in connection with their participation in a Competition (including Final Submissions and Submissions for Final Judgment) after that Competition in accordance with the Competition Information Disclosure Policy (hereinafter, "Information Disclosure Policy") posted on the Site. However, Participants shall observe the following matters when publishing information and may not publish any materials prohibited by the Information Disclosure Policy:

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5. If there is any separate arrangement in relation to the confidential information in a Competition, the provisions of such arrangement shall prevail over the provisions of these Terms.
6. If any dispute occurs between a third party and the Host or the Company due to the breach by any Participant of the provisions of this Article and such other party makes any claim against the Company or the Host, such Participant shall compensate for any damage, loss, expenses (including, but not limited to, attorneys’ fees), lost profits and lost revenues, etc. incurred by the Company and the Host.
7. The provisions of this Article shall survive the termination of the relevant Competition or the Participant’s completion of the procedures for withdrawal from all services provided by the Company, with respect to the Company-Provided Information and the Winner’s Final Submissions and Submissions for Final Judgment for a period of five (5) years thereafter.

Article 5 Prohibited Acts of Participants

1. The Company shall prohibit Participants from engaging in any of the following acts in any Competition:

(1) An act of cracking, cheating, spoofing and other misconduct;
(2) An act of redistributing data files included in the dataset;
(3) An act of directly communicating to, consulting with, making a request to, soliciting or responding to solicitation or other activities to other Participants or the Host (other than the Company) without the involvement of the Company for the purpose of furthering the Participant’s own interests;
(4) Any profitmaking activities using the Competition (including but not limited to solicitation or scouting activities, and use for a third party in educational business, etc.) without the prior approval of the Company in writing or any other manner specified by the Company;
(5) An Act of transferring, offering as collateral or otherwise disposing of the Participants’ standing, or the rights or obligations, as a Participant in any Competition (except with the prior written consent of the Company);
(6) Acts that infringe upon the intellectual property rights, trade secrets or any other rights of third parties; and
(7) Any other act in breach of these Terms, etc.

2. If the Company deems that a Participant has engaged in, or may engage in, any of the prohibited acts as prescribed in the preceding paragraph, the Company may, without prior notice to the Participant, disqualify the Participant from the Competition in which the Participant participates, temporarily suspend the Participant from using some or all of the services provided by the Company, withdraw the Participant’s membership, claim damages from the Participant or take any other measures deemed necessary by the Company.

Article 6 Modification of Terms

1. The Company may modify, add or delete any provisions of these Terms from time to time without the approval of Participants.

October 18, 2021