Through hardware ecological plan, SOPHGO adheres to hardware openness and cooperates with partners to make competitive differentiated hardware ecological achievements, jointly building a win-win new ecosystem.
 
            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
Open Hardware Industry Customization Flexible Choices Co-construction with Win-win Cooperation
Through hardware ecological plan, SOPHGO adheres to hardware openness and cooperates with partners to make competitive differentiated hardware ecological achievements, jointly building a win-win new ecosystem.
