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。
图像分割是图像处理和计算机视觉领域的一个重要课题,广泛应用于场景理解、医学图像分析、机器人感知、视频监控、增强现实和图像压缩等领域。
本赛题旨在为选手提供一个预训练的分割模型UNet及测试数据集。参赛选手无须训练模型,使用算能MLIR开源编译器进行编译、量化及调优,实现UNet模型在算能1684X处理器上的部署。参赛者需要兼顾精度与性能,要求精度dice不低于0.85,平均推理延时小于300ms。
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。
1. 【准备环境】首先准备好TPU-MLIR的环境,参考https://tpumlir.org/docs/quick_start/02_env.html
2. 【下载模型】之后从https://github.com/milesial/Pytorch-UNet中,下载UNet模型,有scale1.0和scale0.5两种,为pth格式
3. 【pth->onnx】在模型前后添加前后处理算子,之后编写脚本,将pth格式模型转化为onnx格式
4. 【onnx->mlir】使用tpu/python/tools下的model_transform.py将onnx格式文件转化为mlir
5. 【mlir->bmodel】使用tpu/python/tools下的model_deploy.py将mlir格式文件转化为bmodel模型
6. 【前向推理】参考【初赛参赛指南】中的代码编写前向推理代码,使得bmodel模型能够在测试数据集上跑通。(可以下载复赛数据集中的图片作为tpu_tester.py 的输入)
6. 【提交】将bmodel文件和前向推理代码tpu_tester.py放入submit文件夹后压缩为zip文件,将submit.zip 提交到yi.chu@sophgo.com
本赛题分为初赛、决赛和决赛三个阶段,具体安排和要求如下:
2022/12/09(00:00)发布大赛赛题,选手可登陆算能官网报名;
2022/01/31(12:00)截止报名组队;
2022/12/25(12:00)开启初赛线上测评,参赛选手需使用指定的UNet模型,下载数据并本地调试算法,使用MLIR编译器将模型转换为fp32bmodel,并添加前后处理程序,实现该模型的应用,并能够正确地处理数据。选手可以向指定邮箱提交前后处理代码和fp32bmodel文件,请将代码和fp32bmodel放到目录后压缩为zip文件发送到yi.chu@sophgo.com(pth、onnx、mlir、npz等无需提交),官方会在后台验证是否通过;
2023/01/31(20:00)截止初赛作品提交,复赛入围资格以能够打通流程,使用MLIR将模型转化为fp32bmodel为准,打通流程即可入围复赛。
2023/02/01-02/28,在复赛阶段,参赛选手通过学习MLIR编译器中的量化工具,将UNet模型转为int8bmodel。最终依据模型的精度和性能作为评价指标进行排名。
2023/02/28(20:00)截止复赛作品提交,排名前20支队伍进入决赛。
2023/03/01-03/15,决赛复现与答辩,选手需输出主观题文档与答辩PPT;
2023/03/15,决赛队伍答辩;
2023/03/16(12:00)公布最终排名。
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。