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
算子打榜赛是算能TPU编程竞赛品牌下的首项赛事,参赛者使用算能AI处理器的指令集对指定的算子题目进行开发。 算子打榜赛将以季度比赛的形式进行,每个赛季选手通过优化算子性能,可以不断更新自己在积分排行榜上的名次,在每个赛季末根据排位情况颁发荣誉称号和奖金。通过本次比赛,大家既可以学习AI处理器的知识与技术,又可以通过编辑代码、 性能验证调优提升个人编程能力,并得到权威认证,还可以与AI领域的专家直接交流,感受处理器高性能技术的魅力!
TPU编程竞赛-第二届获奖名单 | |||
---|---|---|---|
奖项 | 姓名 | 学校/企业 | 分数 |
一等奖 | 王*德 | 阿里巴巴 | 962 |
二等奖 | 方*恒 | 山东大学 | 958 |
二等奖 | 郝*寒 | ZEKU | 928 |
三等奖 | 江*特 | 中国科学院大学 | 878 |
三等奖 | 张*菲 | 太原理工大学 | 739 |
三等奖 | 李*宁 | 中国农业大学 | 666 |
三等奖 | 车*宁 | 中国矿业大学 | 650 |
优秀奖共计13位选手:李*,王*夏,胡*,李*媛,郑*宜,姜*文,徐*香,赵*宇,孙*锋,郭*嵩,牟*影,周*洁,马*源。 |
参赛者使用算能AI处理器的指令集对指定的算子题目进行开发
赛题解析:
https://www.sophgo.com/program-competition/analysis.html?season=AS01
赛制解析:
1. 竞赛主题为算子的实现和性能优化,本赛季共4个算子:Reduce_sum、Rgb2bgr、Transpose、Avg_pooling
2. 每个算子有15组参数,每组参数称为一个case,每个case独立计分
3. 每个case只有实现正确才能进入该case的性能排名环节,前20名选手中性能排名第X位的选手将获得(21-X)分,第20名及20名之后的选手获得1分
4. 每道算子题目得分为该题目的15个case分数总和
5. 参赛者总分为4个算子题目的分数总和
6. 参赛者提交的代码出现编译失败或处理器Hang死等异常情况,视为一次失败提交,当前总分计0分,排名情况也会随之变化
7. 官方每天动态更新并公布参赛者得分与排名情况,参赛者每次代码提交会覆盖前一次的代码,实时得分情况以最新提交为准
8. 赛季末参赛者最终总分核验以最后一次提交代码为准,并根据最终总分进行排名和评奖
1. 大赛奖金为税前金额,算能根据相关法律规定为参赛选手代为扣缴,请以税后实收金额为准。
2. 参赛者对报名时所填写的参赛信息的真实性、合法性、有效性承担全部责任。
3. 参赛者提交的代码会自动经过作弊侦测系统,如有用户被检查出竞赛中存在违规行为,将被取消比赛资格。
4. 竞赛中的违规行为:一人使用多账号提交、多账号提交雷同代码(抄袭)、使用不正当手段影响他人竞赛、竞赛结束前在其他平台公布自己的答案。
5.本次竞赛选手的赛题答案、解题思路等知识产权归主办方所有。