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
CCF大数据与计算智能大赛(简称CCF BDCI)由中国计算机学会于2013年创办,是大数据与深度学习领域的算法、应用和系统大型挑战赛事。算能作为大赛合作方提供了赛题「基于TPU平台实现人群密度估计」及TPU算力硬件支持,吸引了众多企业、高校和科研院所的开发人员参加。
人群密度估计是计算机视觉中的一项重要任务,旨在同时识别各种情况下的任意大小的目标,包括稀疏和杂乱的场景。它主要应用于现实生活中的自动化公共监控,能够在公共安全管理、公共空间设计、数据收集分析等方面发挥重要的作用。
本次比赛致力于人群密度估计任务在TPU平台的落地。参赛者选用预训练的模型部署在算能TPU处理器上,无需自己训练模型。参赛者在实现模型部署的过程中需要兼顾精度与推理速度。
该赛题采用初赛、决赛的“二级赛制”,具体赛程安排如下:
2022/08/29(12:00),发布大赛赛题,选手可登录大赛官网报名;
2022/09/05,开启初赛线上评测,选手可在线提交结果文件至竞赛平台,每日每队最多可提交3次,测评系统将自动评测得分并同步更新至排行榜。排行榜上将记录选手的最高成绩,相关团队必须自行保存最高成绩作品的源代码以备审核;
2022/11/04(12:00),截止报名组队;
2022/11/07(24:00),截止初赛A榜作品提交;初赛A榜评测结束后,所有选手均可进行初赛B榜评测;
2022/11/08,公布B榜测试集,本赛题所有参赛选手务必在11月08日24:00前,在本赛题“赛题数据”页下载B榜测试集;
2022/11/09(00:00-24:00),初赛B榜作品提交,参赛者可在B榜当天提交3次,但仅以每支团队当天最后一次提交进行评测,决赛入围资格以B榜线上最终成绩为准(B榜排行榜展示的成绩),若团队没有进行B榜提交,则无法晋级后续比赛,B榜TOP5团队经复现审核后入围决赛。
2022/11/10-11/20,复现及决赛资料提交,拟入围决赛的团队按照要求提交复现资料、决赛答辩评审资料等;
2022/11/26-11/27,决赛评审(线上);
2023/1月上旬,决赛嘉年华系列活动(时间、场地待定,请关注通知)
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;
报名时所有参赛选手需提供个人基本信息,并进行实名认证;参赛选手应当保证身份信息的真实性。大赛组委会承诺其中涉及个人隐私的内容予以以保密;
为保证每支参赛团队享有有相对平等的提交机会,各赛题组队需满足组队成员在赛题中的提交总次数≤开赛天数*赛题每天提交次数;
大赛出题的人员及所在部门人员禁止参与所出具的赛题(可参与其他赛题),直接参与大赛策划、组织、技术服务的工作人员等相关人士禁止参赛,禁止委托他人参赛或违规指导参赛团队。
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。