SOPHGO AI Computing
SOPHGO SG2002 RISC-V AI Vision Processor
SG2002 combines two RISC-V C906 processing cores, an approximately 1 TOPS INT8 TPU, video codecs, image-signal processing, flexible sensor interfaces, audio and hardware security in an endpoint AI vision processor. It is suited to designs that need local camera intelligence, multimedia processing and secure embedded operation in one device.
Stock, price and lead time: Contact GalaxyIC for confirmation.
SG2002 at a glance
The SG2002 belongs to SOPHGO's endpoint risc-v aiot processor portfolio. Engineers evaluating this device should connect headline compute figures with the complete workload: model precision, memory footprint, video pipelines, host interfaces, latency, concurrency, thermal limits and software support. GalaxyIC uses the exact model identity in every inquiry so that the requested product is not confused with a related member of the same family.
- Main processor
- RISC-V C906 at 1.0 GHz
- Coprocessor
- RISC-V C906 at 700 MHz
- Vector processing
- Integrated floating-point unit (FPU)
- AI performance
- Approximately 1.0 TOPS INT8
- AI frameworks
- Caffe, TensorFlow, TensorFlow Lite, PyTorch, ONNX and MXNet
- Video codecs
- H.264, H.265 and JPEG encode / decode
- Maximum video resolution
- 2880 x 1620
- Video input
- Two simultaneous inputs; MIPI, Sub-LVDS, HiSPI, BT.601 and BT.656
- Video output
- BT.601 / BT.656 / BT.1120, two-lane MIPI-DSI and 8080 / RGB666
- Image processing
- ISP, AE / AWB / AF, WDR, noise reduction, lens correction and digital stabilization
- Audio
- 16-bit voice I/O; G.711, G.726, ADPCM, AEC, ANR and AGC
- Security
- Secure Boot, secure upgrade, AES, DES, SM4, SHA, TRNG and Secure eFuse
- Brand
- SOPHGO
- Supply status
- Contact for confirmation
Specifications shown here summarize the supplied SOPHGO company material. Final design decisions must use the current official data sheet, hardware manual and software release notes for the exact orderable device.
Target applications
SOPHGO SG2002 may be evaluated for smart IP cameras and video doorbells, access-control and attendance terminals, embedded machine-vision cameras, robot and drone vision, retail and industrial monitoring, secure AIoT video endpoints. A suitable design-in depends on more than nominal AI performance. Camera count, resolution, codec load, preprocessing, model architecture, quantization, batch size, context length and peripheral traffic can materially change real throughput.
- Smart Ip Cameras And Video Doorbells
- Access-Control And Attendance Terminals
- Embedded Machine-Vision Cameras
- Robot And Drone Vision
- Retail And Industrial Monitoring
- Secure Aiot Video Endpoints
For international projects, GalaxyIC recommends documenting the end application and the required validation stage. An evaluation board request, prototype build, pilot quantity and production purchase have different documentation, traceability and delivery requirements.
Engineering and integration checklist
- Confirm the exact package, memory configuration, temperature grade and production suffix in the RFQ.
- Match camera sensors and electrical interfaces to the required MIPI, Sub-LVDS, HiSPI or parallel input path.
- Validate the complete ISP, codec and neural-network pipeline at the target 2880 x 1620 or lower operating resolution.
- Review model conversion for Caffe, TensorFlow, TensorFlow Lite, PyTorch, ONNX or MXNet and confirm supported operators.
- Plan Secure Boot, key provisioning, secure upgrade and eFuse handling before production.
Before requesting volume pricing, confirm the exact part or platform, package, operating temperature, memory configuration, board form factor and supported software version. For AI workloads, include the model name, precision, input shape, expected latency or throughput, and number of concurrent streams or users. For RISC-V server projects, include the required operating system, compiler, firmware, networking, storage and expansion environment.
A proof of concept should test the full pipeline rather than an isolated TOPS figure. This includes data ingestion, decoding, preprocessing, inference, post-processing, storage and network output. It also reduces the risk of selecting a device whose theoretical compute is suitable but whose memory, interface or toolchain does not fit the deployed system.
How GalaxyIC handles a SOPHGO SG2002 RFQ
GalaxyIC is an independent electronic-component sourcing and RFQ service. We do not present this page as the official SOPHGO website and do not claim an authorized-distributor relationship. After receiving your inquiry, the sales team reconfirms the exact model identity, requested quantity, material condition, date or lot requirements when applicable, available documentation, lead time and commercial terms.
To receive a useful quotation, send the destination country, target quantity, project schedule and whether the request is for chips, modules, evaluation hardware, accelerator cards or complete systems. If alternatives are acceptable, state the performance, interface and software requirements that cannot change. No substitute should be approved from a marketing description alone.
SOPHGO ecosystem and platform context
SOPHGO develops AI processors, endpoint and edge vision devices, RISC-V server processors, accelerator modules, cards and server platforms. Products such as AS711 and AS713 illustrate how processor, memory, host CPU, storage, interconnect, thermal design and inference software come together as a deployable AI system. The right purchasing level therefore depends on whether the customer is building custom hardware or needs an integrated platform.
Software compatibility is a sourcing requirement, not an afterthought. Ask for the current compiler, runtime, framework support, model-conversion workflow and reference software that correspond to the selected hardware. Toolchain version, operator coverage and quantization accuracy can affect project schedules as much as physical lead time.
Frequently asked questions
Can GalaxyIC confirm SG2002 inventory immediately?
Inventory is not inferred from this SEO page. GalaxyIC confirms availability, quantity, condition, price and lead time after receiving an RFQ.
What should be included in the RFQ?
Include the exact model or suffix, quantity, application, destination, requested delivery date, package or temperature-grade requirements, and whether technical documentation or traceability is required.
Can SG2002 replace another AI processor?
Potential alternatives must be compared at system level. Compute precision, memory, video, I/O, power, form factor and software compatibility all require engineering review and written approval.