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
TPU编程竞赛-应用挑战赛(以下简称“大赛”)是由算能主办的应用挑战竞赛,大赛面向各大高校学生、开源社区开发者等来自各地的参赛选手,旨在根据不同算法和软件技术栈层级逐步设立系列竞赛赛事,并将竞赛赛题开源。TPU编程竞赛作为一个高水平的竞赛平台,致力于为国家发现和培育算法人才、极致挖掘TPU硬件澎湃算力、用算法和软件的创新解决社会或业务问题,激发开源创新活力,培养开源实践人才,助力开源生态建设。
细粒度图像分类(Fine-grained image categorization),是当前计算机视觉、模式识别领域的一个研究热点。ViT(Vision Transformer)模型是Google提出的基于Transformer的高效分类模型,被广泛应用在图像识别、生成建模和多模型任务中,例如对象检测、动作识别、视觉问答、视觉推理等。如今的智能应用场景纷繁复杂,在边端设备上完成模型部署,使其具备高速推理能力,对应用落地而言具有重要意义。本竞赛旨在引导选手完成在边端设备上的ViT模型部署,并提高推理性能,挖掘TPU硬件极致算力,激发开源创新活力。
参赛者需要使用预训练好的ViT模型,使用int8量化、算子融合、图优化、layergroup优化、混合精度等方式对模型完成编译优化,最后使用算能工具链将模型转成算能1684x处理器可执行的bmodel文件,部署到算能少林派上运行;使用工具链分析模型性能,在保证与原始模型余弦相似度大于0.85的情况下,提高模型推理速度。
TPU-MLIR项目是算能智能处理器的TPU编译器工程。该工程提供了一套完整的工具链,其可以将不同框架下预训练的神经网络,转化为可以在算能TPU上高效运算的二进制文件。
本赛题只设置决赛,具体安排和要求如下:
2023/07/06(12:00)发布大赛赛题,选手可登录算能官网报名;
2023/07/12(12:00)截至报名组队;
2023/07/12,决赛复现与答辩,选手输出技术文档与答辩PPT
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。
个人报名信息要求准确有效,否则会被取消参赛资格。本赛事不收取任何报名费用。
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。