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.
16M High-end Intelligent Deep Learning Vision Processor
Based on RISC-V 3-5M slightly intelligent deep learning vision processor
Based on RISC-V 5M light intelligent deep learning vision processor
RISC-V 5M light intelligent deep learning vision processor
5M light intelligent deep learning vision processor
5M light intelligent Deep learning vision processor
BM1688, 16-channel HD Video Analysis
BM1688, 16-channel HD Video Analysis
CV186AH, 8-channel HD Video Analysis
CV186AH, 8-channel HD Video Analysis
BM1684X, 416-channel HD video analysis
BM1684X, 288-channel HD Video Analysis
BM1684X, 32-channel HD Video Analysis
BM1684X, 32-channel HD video intelligent analysis
BM1684X, 32-channel HD video intelligent analysis
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.
Intelligent computing server SGM7-40, adapted to mainstream LLM, a single card can run a 70B large language model
96-channel HD video decoding, 96-channel HD video analysis
32-channel HD video decoding, 32-channel HD video analysis
32-channel HD video decoding, 32-channel HD video analysis
Intelligent analysis of 8-192 channels of 1080p high-definition videos; each channel can support simultaneous operation of 3 algorithms.
The algorithms are diverse, involving full-object analysis, behavior analysis, and other algorithms, which can be called on demand and freely combined.
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
Empower prison management with intelligent monitoring of key controlled areas through smart video analysis
Using deep learning intelligent analysis to monitor scenarios such as safety production, urban firefighting, and unexpected incidents for emergency regulation.
Using specific deep learning algorithms to watermark, blur, or apply other methods to streaming videos, achieving video confidentiality and preventing leaks
SOPHGO with the SOPHON.TEAM ecosystem to build a data intelligence content governance solution.
Using deep learning technology to detect and analyze individuals, vehicles, and security incidents in grassroots governance
Real-time compression and transcoding of video to the cloud and monitoring of abnormal events, enhancing the ability to detect and handle road safety incidents
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
To rapidly construct business capabilities that integrate multidimensional data including people, vehicles, and traffic flow for users
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
Providing deep learning capabilities for the financial, insurance, and various business service industries to enhance operational efficiency and improve service quality
SOPHGO with SOPHON.TEAM ecosystem partners to offer a "Deep Learning Video Analysis + Restaurant Front-of-House Management" solution
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
Provided safety monitoring solutions for violations and abnormal events in offices, quality inspection, weighing rooms, storage areas and other areas of large storage parks such as granaries and cotton warehouses
SOPHON.TEAM is collaborating with ecological partners to develop a comprehensive solution for ensuring the safety of tobacco industry production and control
In collaboration with SOPHON.TEAM and its ecological partners, SOPHGO utilizes domestically developed computing power products as the hardware foundation to build a safety production management system and improve the safety production management level of liquor enterprises
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
The Huashan Pi - CV1812H development board is an open-source ecosystem jointly launched by Algonomy and its hardware partners, providing users with an open-source development environment based on RISC-V. It focuses on visual and intelligent scene development, aiming to grow together with developers.
The Huashan Pi is an evaluation board designed for the CV1812H multimedia processing processor. It is used to develop robust peripheral interfaces for CV1812H. Additionally, it offers hardware reference designs based on CV1812H, allowing customers to swiftly complete product hardware designs based on the development board circuit.
The development board can be connected to a computer via a USB cable, serving as a basic development system or a more comprehensive one. The supported devices and peripherals for development include:
For more Huashan Pi application courses, please check at the SOPHON Academy:
https://www.sophgo.com/curriculum/description.html?category_id=5
Participants affirm the originality and authenticity of their submitted proposals and outcomes. Any intellectual property disputes arising from this will be the responsibility of the participant.
(1) Project Overview: Introduction to the overall work
(2) Proposal Description: Introduction to the proposal's architecture (including software and hardware content)
(3) Detailed Presentation: Introduction to various functional modules and implementation methods
(4) Proposal Demonstration: Explanation of the demo's performance in actual testing scenarios
After the project concludes, submit the work's source code via the contest registration page's work submission portal.
- Technical Feasibility: Proposed technical solutions based on the development board should be accompanied by corresponding software and hardware architectural diagrams.
- Comprehensive Proposal: Creative content should be no less than 300 words, covering, but not limited to, five aspects: creative background, theme, approach, problem-solving ideas, and detailed implementation plans.
- Innovation: Uniqueness from existing market products, innovative perspectives, and a distinct proposal.
- Originality: Proposal ideas and technical sharing blogs must be team-original without plagiarism or infringement. Otherwise, the team will be disqualified.
- Completeness of Work (35 points): Reasonable system design, comprehensive proposal, and capable of achieving basic functions.
- Demonstration Video (35 points): Clear, engaging, and narrative video.
- Innovation (15 points): Innovation, applicability, interest, and scalability of the proposal.
- Technical Exploration (15 points): Technical exploration or breakthrough based on keywords such as Vector 1.0, HHB, Android.
- Publishing technical blogs: A minimum of 300 words with 3 images per post can earn 2 points each. A maximum of 6 cumulative points can be earned, not exceeding 6 points regardless of the number of posts.
- Submission of source code for the creative project: Earns 4 points.