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
全国大学生计算机系统能力大赛(以下简称“大赛”)是由系统能力培养研究专家组发起、由全国高校计算机教育研究会主办、面向高校大学生的全国性大赛。
大赛目标是以学科竞赛推动专业建设和计算机领域创新人才培养体系改革,培育我国高端处理器、关键基础软件的后备人才。大赛鼓励学生设计、实现综合性的计算机系统,培养系统级的设计、分析、优化与应用能力,提升学生的技术创新、工程实践、团队协作能力。大赛服务国家人才培养战略,以赛促学、以赛促教,为高水平计算机人才成长搭建交流、展示、合作的开放平台。
1. 2023年3月15日:报名启动。
2. 2023年4月30日:报名截止。
3. 对于" OS 内核实现"赛道,分批发放实验硬件平台。
赛题及评审指标发布:
对于" OS 内核实现"赛道,2023年3月28日发布参赛题目和区域赛阶段的评测技术指
对于" OS 功能挑战"赛道,2023年3月28日发布所有参赛题目和区域赛评审的参考考核目标。
对于" OS 内核实现"赛道,自赛题发布之日起至2023年5月31日前,参赛队完成区域赛题目。区域赛过程中,要求源代码与开发过程全部开源展示,参赛队应按要求提供开源发布的网址以供访问,并将相关内容提交到操作系统设计赛指定的托管平台上。
对于" OS 功能挑战"赛道,自赛题发布之日起至2023年5月31日前,参赛队按照题目所述技术要求给出实现方案,并提交作品源代码和开发文档。2023年5月14/15日为区域赛阶段中间考核节点,参赛队应按要求提交项目的阶段性技术报告和开发状态演示,以便评审专家进行阶段性检查,并给出指导性建议。操作系统设计赛要求源代码与开发过程全部开源展示,参赛队应按要求提供开源发布的网址以供访问,并将相关内容提交到操作系统设计赛指定的托管平台上。
2023年6月1日﹣6月10日,操作系统设计赛评审委员会对两个赛道的区域赛作品进行评审,6月10日公布区域赛成绩及全国赛入围名单。
报名和区域赛期间会安排技术培训和技术报告。
1." OS 内核实现"赛道
2023年6月10日:发布全国赛题目,公布全国赛的评测指标。
全国赛第一阶段:2023年6月10日﹣8月1日,参赛队应按要求公开现阶段源代码和设计实现文档,评审委员会根据全国赛提交的源代码和测试结果给出各参赛队的成绩排名(具体时间以组委会公布的为准)。
全国赛第二阶段:参赛队完成指定题目(具体时间以组委会公布的为准)。
答辩、颁奖典礼和操作系统设计赛闭幕式(具体时间以组委会公布的为准)。
2." OS 功能挑战赛道"
全国赛第一阶段:2023年6月10日﹣8月15日,参赛队完成并最终提交项目代码和项目设计与实现文档,评审专家进行检查(具体时间以组委会公布的为准)。·全国赛第二阶段:参赛队针对题目做整体展示,评审委员会给出成绩(具体时间以组委会公布的为准)。
1. 参赛队是操作系统设计赛报名参赛的基本单位,每支参赛队的人数为1~3人,同一高校的参赛队数量没有限制,但是来自不同学校的学生不能联合组队参赛。
2. 参赛队可选择参加" OS 内核实现"赛道或" OS 功能挑战"赛道,每个参赛队只能选择一个赛道参赛,并在报名时选定参赛题目。
3. 每位参赛学生只能参加一支参赛队,不可重复报名。
4. 每个参赛队至少要有一位指导教师,最多有两位指导教师。指导教师可以是高校教师或企业技术专家,每位指导教师可同时指导本校或外校多支参赛队。指导教师负责指导参赛队选题、组织学生参加赛前技术培训,并鼓励学生根据选定的操作系统设计赛题目进行相关的创新设计与实现,同时负责在操作系统设计赛过程中与学校及操作系统设计赛组委会之间的信息沟通。
5. 在2023年5月30日之前,各参赛队有一次机会调整参赛队员。参赛队员的调整只能更换一名队员,并将调整前后的队员名单报送至操作系统设计赛组委会。5月30日之后,只能减少参赛队员,不能更换、增加参赛队员。