TPU processor, 16 channels HD video intelligent analysis, 32 channels of full HD video decoding
TPU processor, 32 channels HD video intelligent analysis, 32 channels of full HD video decoding, 12 channels of full HD video encoding
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.
Based on RISC-V 3-5M slightly intelligent deep learning vision processor
Based on RISC-V 5M light intelligent deep learning vision processor
RISC-V 5M light intelligent deep learning vision processor
5M light intelligent deep learning vision processor
4K Super Definition Deep learning vision processor
5M high-performance Deep learning vision processor
5M light intelligent Deep learning vision processor
960-channel HD video decoding, 480-channel HD video analysis
576-channel HD video decoding, 288-channel HD video analysis
BM1684X, 416-channel HD video analysis
X86 host processor,288-channel HD video analysis
BM1684X, 32-channel HD Video Analysis
BM1684, 16-Channel HD Video Analysis
BM1684, 192-channel HD video analysis
BM1684, 8-channel HD video analysis
CV186AH, 8-channel HD Video Analysis
BM1688, 16-channel HD Video Analysis
72-channel HD video decoding, 72-channel HD video analysis
96-channel HD video decoding,48-channel HD video analysis
32-channel HD video decoding,16-channel HD video analysis
32-channel HD video decoding, 32-channel HD video analysis
32-channel HD video decoding, 32-channel HD video analysis
32-channel HD video decoding, 16-channel HD video analysis
32-channel HD video decoding, 16-channel HD video analysis
Deep Learning Developer Product Portfolio
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
Empower prison management with intelligent monitoring of key controlled areas through smart video analysis
Using deep learning intelligent analysis to monitor scenarios such as safety production, urban firefighting, and unexpected incidents for emergency regulation.
Using specific deep learning algorithms to watermark, blur, or apply other methods to streaming videos, achieving video confidentiality and preventing leaks
SOPHGO with the SOPHON.TEAM ecosystem to build a data intelligence content governance solution.
Using deep learning technology to detect and analyze individuals, vehicles, and security incidents in grassroots governance
Real-time compression and transcoding of video to the cloud and monitoring of abnormal events, enhancing the ability to detect and handle road safety incidents
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
To rapidly construct business capabilities that integrate multidimensional data including people, vehicles, and traffic flow for users
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
Providing deep learning capabilities for the financial, insurance, and various business service industries to enhance operational efficiency and improve service quality
SOPHGO with SOPHON.TEAM ecosystem partners to offer a "Deep Learning Video Analysis + Restaurant Front-of-House Management" solution
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
Provided safety monitoring solutions for violations and abnormal events in offices, quality inspection, weighing rooms, storage areas and other areas of large storage parks such as granaries and cotton warehouses
SOPHON.TEAM is collaborating with ecological partners to develop a comprehensive solution for ensuring the safety of tobacco industry production and control
In collaboration with SOPHON.TEAM and its ecological partners, SOPHGO utilizes domestically developed computing power products as the hardware foundation to build a safety production management system and improve the safety production management level of liquor enterprises
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。