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
由中国深度学习学会和浙江省大数据发展管理局联合主办的《社会计算创新大赛》,算能作为赞助方与硬件提供商,致力于培养深度学习领域的人才、鼓励高校学生用科技解决实际难题、进一步推动深度学习应用场景落地。
社会计算创新竞赛获奖名单 | ||
---|---|---|
队伍名称 | 名次 | 代表队员 |
马路杀手 | 1 | 田*文 |
数据跳动 | 2 | 黄* |
mango | 3 | 周*宇 |
穿梭在银河的火箭队 | 4 | 郭*月 |
嘉豪在干嘛 | 5 | 邱*峰 |
1. 学生根据参考资料完成目标检测算法在算能SE5微服务器上的部署,有余力的参赛学生可以自行优化模型;
2. 学生需自行设计程序用于统计给定视频中的人数。该程序首先必须在SE5上运行,其次必须具备输入接口(调用该接口能够输入一个mp4文件),最后返回该视频中出现的总人数(备注:重复出现的人数只算一次)。
大赛面向全球征集参赛团队,不限年龄、国籍,高校、科研院所、企业从业人员等均可登录官网报名参赛。
每队1-5人,每个人最多组队一次,不可退出队伍。
选手通知:大赛组委会将通过参赛团队预留的联系方式邀请参赛团队参与大赛各项活动,若参赛团队在相关通知发出后3日内未答复,则视为自动放弃相应机会,主办方有权顺位递补其他参赛团队;
选手获奖:在比赛结束后六个月之内将会将奖金发送到获奖者账户中。
参赛团队在比赛过程中需要自觉遵守参赛秩序,禁止使用规则漏洞、技术漏洞、手动打标等不良途径提高成绩与排名,也禁止在比赛中抄袭他人代码、串通答案、开小号,如果被发现就会被取消比赛资格,并终身禁赛。