TPU processor, 16 channels HD video intelligent analysis, 16 channels of full HD video decoding, 10 channels of full HD video encoding
TPU processor, 32 channels HD video intelligent analysis, 32 channels of full HD video decoding, 12 channels of full HD video encoding
RISC-V + ARM intelligent deep learning processor
Based on the RISC-V core, operating at a frequency of 2GHz, the processor features a single SOC with 64 cores and 64MB shared L3 cache.
SRC1-10 is an excellent performance server cluster based on RISC-V arch. It has both computing and storage capabilities, and the full stack of software and hardware is domestically produced.
The RISC-V Fusion Server, supports dual-processor interconnection and enabled intelligent computing acceleration.
SRB1-20 is an excellent performance storage server based on RISC-V arch. It supports CCIX, 128-core concurrent, multi-disk large-capacity secure storage, and the full stack of software and hardware is domestically produced.
SRA1-20 is an excellent performance computing server based on RISC-V arch. It supports CCIX, 128-core concurrent, both software and hardware are open source and controllable.
SRA3-40 is a RISC-V server for high-performance computing, domestic main processor,excellent performance,fusion of intelligent computing, support powerful codec.
SRB3-40 is a high-performance RISC-V storage server with multiple disk slots and large-capacity secure storage.
Intelligent computing server SGM7-40, adapted to mainstream LLM, a single card can run a 70B large language model
SOM1684, BM1684, 16-Channel HD Video Analysis
Core-1684-JD4,BM1684, 16-Channel HD Video Analysis
SBC-6841,BM1684, 16-Channel HD Video Analysis
iCore-1684XQ,BM1684X,32-Channel HD Video Analysis
Core-1684XJD4,BM1684X,32-Channel HD Video Analysis
Shaolin PI SLKY01,BM1684, 16-Channel HD Video Analysis
QY-AIM16T-M,BM1684, 16-Channel HD Video Analysis
QY-AIM16T-M-G,BM1684, 16-Channel HD Video Analysis
QY-AIM16T-W,BM1684, 16-Channel HD Video Analysis
AIV02T,1684*2,Half-Height Half-Length Accelerator Card
AIO-1684JD4,BM1684, 16-Channel HD Video Analysis
AIO-1684XJD4,BM1684X,32-Channel HD Video Analysis
AIO-1684XQ,BM1684X,32-Channel HD Video Analysis
IVP03X,BM1684X,32-Channel HD Video Analysis
IVP03A,Microserver, passive cooling, 12GB RAM
Coeus-3550T,BM1684, 16-Channel HD Video Analysis
EC-1684JD4,BM1684, 16-Channel HD Video Analysis
CSA1-N8S1684,BM1684*8,1U Cluster Server
DZFT-ZDFX,BM1684X,Electronic Seal Analyzer,ARM+DSP architecture
ZNFX-32,BM1684, 16-Channel HD Video Analysis
ZNFX-8,BM1684X,ARM+DSP architecture,Flameproof and Intrinsic Safety Analysis Device
EC-A1684JD4,Microserver with active cooling, 16GB RAM, 32GB eMMC
EC-A1684JD4 FD,BM1684, 16-Channel HD Video Analysis,6GB of RAM, 32GB eMMC
EC-A1684XJD4 FD,BM1684X,32-Channel HD Video Analysis
ECE-S01, BM1684, 16-Channel HD Video Analysis
IOEHM-AIRC01,BM1684,Microserver Active Cooling,16-Channel HD Video Analysis
IOEHM-VCAE01, BM1684, 16-Channel HD Video Analysis
CSA1-N8S1684X,BM1684*8,1U Cluster Server
QY-S1U-16, BM1684, 1U Server
QY-S1U-192, BM1684*12, 1U Cluster Server
QY-S1X-384, BM1684*12, 1U Cluster Server
Deep learning intelligent analysis helps make city management more efficient and precise
Using deep learning video technology to analyze sources of dust generation and dust events, contributing to ecological environmental protection
Using deep learning intelligent analysis to monitor scenarios such as safety production, urban firefighting, and unexpected incidents for emergency regulation.
Using deep learning technology to detect and analyze individuals, vehicles, and security incidents in grassroots governance
Empowering the problems of traffic congestion, driving safety, vehicle violations, and road pollution control
Utilizing domestically developed computational power to support the structured analysis of massive volumes of videos, catering to practical applications in law enforcement
Build a "smart, collaborative, efficient, innovative" gait recognition big data analysis system centered around data
Effectively resolving incidents of objects thrown from height, achieving real-time monitoring of such incidents, pinpointing the location of the thrown object, triggering alerts, and effectively safeguarding the safety of the public from falling objects
Using edge computing architecture to timely and accurately monitor community emergencies and safety hazards
SOPHGO with SOPHON.TEAM ecosystem partners to build a deep learning supervision solution for smart hospitals, enhancing safety management efficiency in hospitals
SOPHGO with SOPHON.TEAM ecosystem partners to build a smart safe campus solution
Using a combination of cloud-edge deep learning methods to address food safety supervision requirements across multiple restaurant establishments, creating a closed-loop supervision system for government and enterprise-level stakeholders
SOPHON's self-developed computing hardware devices, such as SG6/SE5/SE6, equipped with SOPHON.TEAM video analysis algorithms, are used to make industrial safety production become smarter
Combining deep learning, edge computing and other technologies, it has the ability to intelligently identify people, objects, things and their specific behaviors in the refueling area and unloading area. It also automatically detects and captures illegal incidents at gas stations to facilitate effective traceability afterwards and provide data for safety management.
SOPHGO, in collaboration with SOPHON.TEAM and its ecosystem partners, is focusing on three major scene requirements: "Production Safety Supervision," "Comprehensive Park Management," and "Personnel Safety & Behavioral Standard Supervision." Together, they are developing a comprehensive deep learning scenario solution, integrating "algorithm + computing power + platform."
SOPHGO, cooperates with SOPHON.TEAM ecological partners to build a deep learning monitoring solution for safety risks in chemical industry parks
SOPHGO with SOPHON.TEAM ecosystem partners to build a Smart Computing Center solution, establishing a unified management and scheduling cloud-edge collaborative smart computing center
SOPHGO, in collaboration with SOPHON.TEAM ecosystem, have jointly developed a set of hardware leveraging domestically-produced deep learning computational power products. This is based on an AutoML zero-code automated deep learning training platform, enabling rapid and efficient implementation of deep learning engineering solutions
The Global Campus Artificial Intelligence Algorithm Elite Competition has been held for four consecutive sessions since 2019, attracting contestants from 26 countries and regions around the world, as well as over 900 universities. With a total of 10152 participating teams, it has received widespread attention from global campus artificial intelligence algorithm enthusiasts and the industry. In March 2023, the competition was included for the first time in the national ranking of subject competitions in ordinary higher education institutions released by the Chinese Association of Higher Education, officially becoming a national subject competition.
In order to promote talent cultivation under the "Artificial Intelligence+X" knowledge system, adhere to the principle of "promoting learning through competitions, teaching through competitions, and innovation through competitions", accelerate the cultivation of innovative talents in artificial intelligence algorithms, stimulate students' awareness of artificial intelligence innovation and enthusiasm for participating in innovative application practices, and promote high-quality entrepreneurship and employment of university students, the organizing committee of the competition has decided to continue holding the fifth "Global Campus Artificial Intelligence Algorithm Elite Competition".
1、图像文字说明生成算法比赛的目标为创建一个模型来预测给定生成图像的文本提示。参赛选手将在包含 Stable Diffusion2.0 生成的各种(提示、图像)对的数据集上进行预测,通过了解潜在存在的提示、图像之间关系的可逆性。参赛选手通过构建一个模型来预测给定生成图像的文本提示。并把这个文本提示与标注过的文本提示进行对比。
2、提示工程(Prompt Engineering)是一种针对预训练语言模型(如 ChatGPT),通过设计、实验和优化输入提示来引导模型生成高质量,准确和有针对性的输出的技术。由此产生的提示学习是一种通过构建合适的输入提示来解决特定任务的方法。本赛题就是通过构建数据模型来研究如何提升提示工程的效果。
3、文本到图像模型的流行已经是基于提示工程的一个深度学习全新领域。用户体验的一部分是艺术,一部分是充满不确定性的数据科学,机器学习工程师正在迅速努力理解提示和它们生成的图像之间的关系。在提示中添加“4k”是使其更具照片感的最佳方式吗?提示中的微小扰动是否会导致图像高度发散?提示关键字的顺序如何影响生成的场景?这项竞赛的任务是创建一个模型,该模型可以可靠地反转生成给定图像的扩散过程。
4、本赛题任务是预测用于生成目标图像的提示。这个挑战的提示是使用各种(未公开)方法生成的,从简单到复杂,都有多个对象和修饰符。使用 Stable Diffusion 2.0(768-v-ema.ckpt)根据提示生成图像,并以 768x768 像素的 50 步生成图像,然后将竞争数据集的图像缩小到 512x512。使用了此脚本,参见参考代码库。
1. Opening time of the competition registration system: September 25, 2023;
2. Registration deadline: 8 pm on November 20, 2023;
3. Completion time of school competition: before November 25, 2023;
4. Completion time for provincial competitions (regional competitions, national preliminary evaluations): before November 30, 2023;
5. National Finals Time: Before December 8, 2023;
6. The venue and format of the competition will be notified separately.
1、省赛能够做运算力平台上活动比较好的成绩,依据系统 自动评分获得奖励;(100 分)
2、每个获奖队伍能够获得 1000 元的算能积分大礼包;
3、冠军队能够获得校招面试直通券;
4、获奖队伍活动实习终面直通券;
5、TPU 编程竞赛委员会加入邀请函;
6、企业参访计划。
1、总决赛评分规则
(1)省赛能够在边缘运算力平台上活动比较好的成绩,依据 系统自动评分获得奖励(60 分);
(2)能够基于这个提升工程应用,提出相应合理的应用场景 (20 分);
(3)完美现场(线上/线下)讲解和答辩(20 分)。
2、提供丰厚奖品
一等奖 5000 元,每个队能够获得一个能跑大模型的边缘盒子(边缘算力盒子), 一名
二等奖 3000 元,每个队能够获得一个能跑大模型的边缘盒子(边缘算力盒子) ,二名
三等奖 1000 元,每个队能够获得一个能跑大模型的边缘盒子(边缘算力盒子), 三名
(1)每个获奖队伍能够获得 1000 元的算能积分大礼包;
(2)冠军队能够获得校招面试直通券;
(3)获奖队伍活动实习终面直通券;
(4)TPU 编程竞赛委员会加入邀请函;
(5)企业参访计划。
1、省赛能够做运算力平台上活动比较好的成绩,依据系统 自动评分获得奖励;(100 分)
2、每个获奖队伍能够获得 1000 元的算能积分大礼包;
3、冠军队能够获得校招面试直通券;
4、获奖队伍活动实习终面直通券;
5、TPU 编程竞赛委员会加入邀请函;
6、企业参访计划。
1、总决赛评分规则
(1)省赛能够在边缘运算力平台上活动比较好的成绩,依据 系统自动评分获得奖励(60 分);
(2)能够基于这个提升工程应用,提出相应合理的应用场景 (20 分);
(3)完美现场(线上/线下)讲解和答辩(20 分)。
2、提供丰厚奖品
一等奖 5000 元,每个队能够获得一个能跑大模型的边缘盒子(边缘算力盒子), 一名
二等奖 3000 元,每个队能够获得一个能跑大模型的边缘盒子(边缘算力盒子) ,二名
三等奖 1000 元,每个队能够获得一个能跑大模型的边缘盒子(边缘算力盒子), 三名
(1)每个获奖队伍能够获得 1000 元的算能积分大礼包;
(2)冠军队能够获得校招面试直通券;
(3)获奖队伍活动实习终面直通券;
(4)TPU 编程竞赛委员会加入邀请函;
(5)企业参访计划。