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
(1)硬件平台
少林派开发板
少林派开发板是一款基于BM1684的约20TOPS算力开发平台,以BM1684作为核心器件,核心处理器全自主可控,提供超强算力+多路视频编解码能力。支持3路mini-PCIe,4路USB。可扩展多种外设模块。可以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。硬件生态丰富,可连接的外设多样。支持丰富的软件开发生态体系,支持主流深度学习框架。“少林派”核心板可以扩展屏幕、键盘、鼠标、摄像头、耳机、VR等各种设备。可以在“少林派”上DIY一个全场景的边缘计算工作站,实践各种实验。也可以嵌入到无人车和无人机中,实现移动终端的边缘计算。
少林派开发板支持的外扩模块如下:
MiNi PCIe转WiFi6&蓝牙5.2
MiNi PCIe转4G模块
MiNi PCIe转USB3.0*2
MiNi PCIe转GE RJ45*2
MiNi PCIe转SFP
MiNi PCIe转HDMI
MiNi PCIe转CAN*2
MiNi PCIe转SATA
参赛选手可以选配以上外扩模块来完成自己的参赛作品,也可以自己设计外扩模块。
公司基于少林派开发板开发了KT001智能车和S550深度学习无人机,参赛选手也可以申请深度学习车和深度学习无人机作为硬件平台完成自己的参数作品。
KT001智能车
S550深度学习无人机
(2)软件平台
SophonSDK是算能科技基于其自主研发的处理器所定制的深度学习SDK,涵盖了神经网络推理阶段所需的模型优化、高效运行时支持等能力,为深度学习应用开发和部署提供易用、高效的全栈式解决方案。
SophonSDK 由 Compiler和Library组成:
Compiler 负责对第三方深度学习框架下训练得到的神经网络模型进行离线编译和优化,生成最终运行时需要的 BModel。目前支持Caffe、Darknet、MXNet、ONNX、PyTorch、PaddlePaddle、TensorFlow等。
Library提供了BM-OpenCV、BM-FFmpeg、BMCV、TPURuntime、BMLib等库,用来驱动VPP、VPU、JPU、TPU等硬件,完成视频图像编解码、图像处理、张量运算、模型推理等操作,供用户进行深度学习应用开发。
算能提供了SDK相关的资料供选手学习使用:
1. 文档中心:https://developer.sophgo.com/site/index/material/30/all.html
2. 视频教程:https://developer.sophgo.com/site/index/course/all/all.html
3. 开发指南:https://sophgo-doc.gitbook.io/sophonsdk3
各位算能杯的参赛选手,如果您已经成功报名算能杯(不必等到报名截止时间)即可申请设备借用,公司有SLKY01少林派开源硬件、KT001智能车、S550无人机等硬件可供选择。
如需借用,直接在网站上下单即可,下单时需要支付押金。下单后联系硬十客服备注借货,留下姓名、联系方式和报名成功的截图凭证(盖章的报名表及参赛保证金的支付截图)。硬十客服的微信号:yingshi_mm。
KT001智能车的借用链接如下:
https://www.hw100k.com/coursedetail?id=176
S550无人机的借用链接如下:
https://www.hw100k.com/coursedetail?id=179
SLKY01少林派开源硬件的借用链接如下:
https://www.hw100k.com/coursedetail?id=177
赛程结束后,请在10个工作日内完整归还借用的设备。公司收到设备并确认完好无损后,在10个工作日内退还押金。
如拿到借货设备后未成功参赛,需尽快退回借货设备,并保证借货设备完好无损,公司将在10个工作日内退还押金。
借货和归还的流程如下:
具体见组委会要求
(1) 系统方案介绍PPT
(2) 方案介绍与功能演示视频
(3) 方案设计与算法实现文档
(4) 带注释的工程源代码
同上
同上
大项 |
内容 |
分值 |
评分要求 |
---|---|---|---|
方案设计阶段 |
TPU处理器边缘计算应用场景的创新性 |
15 |
0~5分:完成一个完整的系统,算法运用了TPU的算力,能够实现市面上已经实现的一些识别算法的应用。 5~10分:作品涉及的应用场景有别于传统的边缘计算场景,有助于发掘TPU处理器的新应用 10分~15分:系统完整,工作稳定可靠性,具备独特创新性,达到可以申请发明专利程度,算法及算法应用都具备创新性,并且具有商业价值。 |
设计流程 |
5 |
0~2分:能够提供完整的电路设计图纸和源代码,设计报告。 2~5分:基于开发板独立完成系统设计、软硬件开发环境搭建、常用模型的导入和转化、实现目标检测和识别。交付文档描述清晰,表述流畅。 |
|
系统功能性和可扩展性 |
10 |
0~2分:在开发板之外,进行了外设开发。 2~5分:作品在满足特定功能的前提下,具备接口扩展、功能扩展、应用扩展等多层面的延伸性。在提供的开发板之外增加了大量的外设及执行器。 5~10分:具备完整的硬件、结构、热设计、软件设计。系统的功能扩展具有足够的技术合商业价值,能够形成完整的产品交付水准。 |
|
编译器的使用 |
10 |
5分:参赛者使用算能提供的编译器工具实现模型迁移,将原始模型转换为能在TPU上运行的BMODEL。 5~8分:参赛者使用算能提供的编译器工具实现应用开发部署,能够自行完整的实现训练到推理全流程开发过程。 8~10分:当所用算子(网络层)不被编译器所支持时,参赛者使用编译器提供的插件开发自定义算子(网络层),实现正常编译,且能够提供完整的算子(网络层)描述文档。 |
|
算法的创新性 |
10 |
0~5分:在算能已经支持的算法之外,移植了新的算法并能够稳定运行。 5~10分:所提供的新算法具有创新性,性能优于业界常用的算法。 |
|
系统实现阶段 |
硬件平台搭建 |
10 |
0~5分:在提供的开发板之上,进行了新硬件开发,提供了PCIe或者USB外设,设计合理,具备实用价值。 5~10分:开发开发板外设之外,以开发板作为核心,完成一个完整的硬件系统的交付,可以是各种智能系统、机器人之类 1)外扩模块的选择或者设计是否合理 2)硬件设计合理,接口逻辑匹配 3)硬件系统稳定运行 4)硬件系统具备实用价值及性价比 |
功能实现及完善 |
20 |
作品能够实现完整的功能,效果达到或者超出预期 |
|
作品输出及形式 |
硬件电路、详细设计文档和软件代码 |
15 |
1)设计方案合理、逻辑清晰 2)软件代码规范、完整 3)模块设计内容详细、充分 |
系统演示 |
5 |
1)现场答辩和问答表现 2)作品现场演示效果 |
|
具备产业化及商用价值 |
加分项 |
具有实际应用价值及市场推广价值的作品,或在某特定领域实现首创应用的作品将获得加分 |