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
第七届全国大学生集成电路创新创业大赛是由教育部、中国电子学会主办的全国性比赛。该比赛旨在推动高校集成电路教育改革和创新创业教育,培养高素质的电子信息人才,推动中国集成电路产业的发展。
近几年,边缘计算的重要性日益凸显,作为产业的重要基础设施,边缘计算在边缘侧可以提供低时延、高性价比的智能化服务,在众多垂直行业得到了广泛的应用。
边缘计算使计算和数据存储更靠近收集数据的设备,能实现数据高频交互、实时传输,很好地解决了边缘应用的性能问题。
算能以自研专用处理器TPU(Tensor Processing Unit, 张量处理器)为核心打造了覆盖“云、边、端”全场景的算力产品矩阵,为城市大脑、智算中心、智慧安防、智慧交通、安全生产、工业质检、智能终端等应用提供算力产品及整体解决方案。TPU与同期的处理器和graphic相比,可以提供15-30倍的性能提升,在边缘场景中优势明显。
算能杯主要考察参赛选手利用算能TPU平台实现创新应用的能力,重点场景为:机器视觉以及机器视觉在机器人、无人机等场景中的检测和识别等。
要求选手基于TPU实现新应用和新算法,可以围绕下列领域(包含但不限于)展开:安全生产、通用园区、智慧食品安全、智慧城管、智慧电力、公共安全、智慧交通、智慧煤矿、机器人、无人机、机器视觉等,参赛选手需要自行搭建整套边缘计算系统,并实现相应功能。
关于本次比赛更详细的介绍请移步官网 http://univ.ciciec.com/nd.jsp?id=571#_jcp=1
算能杯钉钉群:12640028543
硬件环境
以BM1684处理器为核心的开发板为基础,设计中必须要把TPU的加速特性应用起来,体现TPU的独特优势,充分利用TPU处理器的超强算力。根据场景需求实现最优配置,最合理成本,最优能耗,最优功能选择。可以自行开发相关硬件,作为系统外设,或者添加FPGA等处理器作为TPU的异构加速器实现更多功能和更优算法。
选题内容:
基于TPU实现新应用和新算法,可以围绕下列领域(包含但不限于)展开:安全生产、通用园区、智慧食品安全、智慧城管、智慧电力、公共安全、智慧交通、智慧煤矿、机器人、无人机、机器视觉等。搭建整套边缘计算系统,并实现相应功能。
本届大赛唯一官方报名平台为赛氪(https://www.saikr.com/vse/univ/ciciec/7),该页面仅作为展示介绍用,对本次比赛感兴趣的选手请移步赛氪报名。
本届大赛采用初赛+分赛区决赛+全国总决赛赛制。全国划分为若干赛区,学生根据学校所在省份参加分赛区决赛,最后优胜者进入全国总决赛。
建议参赛院校/研究所指定一名参赛院校联络负责人负责本单位参赛联络组织工作。
大赛设置校内选拔赛环节,如果学校总体报名队伍不少于20支,参赛院校可申请校内选拔赛;若校内选拔赛获得批准,则所在学校的参赛队伍必须参加校内选拔赛。组委会根据学校的校内选拔赛结果,确定进入初赛环节的名单。校内选拔赛的形式由学校自行决定,校内选拔赛成绩不计入后续成绩。
(1) 报名时间:2023年1月-2023年3月15日,报名截止日期:2023年3月15日。
(2) 作品设计时间:2023年1月-2023年5月
(3) 校园选拔赛时间(可选):2023年5月30日之前
(4) 作品提交截止时间:2023年6月1日
(5) 初赛评审时间:2023年6月
(6) 分赛区比赛:2023年7月
(7) 全国总决赛:2023年8月,重庆(具体时间、地点待定)。
1.参赛作品严禁抄袭、盗用、提供虚假材料或违反相关法律法规,一经发现,参赛选手将丧失参赛相关权利并自行承担一切法律责任。
2.参赛作品中如借鉴或采用他人公开发表的论文和成果(包括所在室验室的已有成果)需征得同意及注明具体来源,并在技术文档、答辩PPT等参与评审打分的材料和答辩过程中主动注明所采用的部分,包括但不限于设备申请、初赛、分赛区决赛、全国总决赛等环节中的答辩以及要求提交的材料等。如未注明,一经发现将做扣分处理。
3.允许非团队成员进行技术指导,但不可代替团队成员参与赛事流程,包括但不限于签到、答辩、现场作品搭建以及作品演讲等,一经发现将取消参赛资格。
4.大赛组委会及其工作人员应尊重和保护参赛团队的知识产权,对参赛团队提交的材料拥有使用权和展示权,全国总决赛结束后,组委会有权整理出版优秀作品文集。
5.参赛团队报名参加企业杯赛,即同意公布于大赛官网的本杯赛题目中关于参赛及作品知识产权的所有条款并承担对应法律责任。默认情况下,杯赛出题企业有权在同等条件下优先购买参加本企业杯赛及单项奖获奖团队作品的知识产权,如有特殊要求,以此杯赛杯赛题目说明为准。
6. 参赛团队报名参赛,即同意“集创赛参赛须知”中关于具体参赛安排的各项规定并承担对应法律责任。
7.全国大学生集成电路创新创业大赛组委会保留对本章程的最终解释权。