EN
EN
Dual-mode split desktop-level design, flexible and easy application and development
For developers who first use SOPHON BM1684 third-generation Deep learning processor series of computing hardware products (including SM5 edge modules, SC5+ accelerator cards, edge computing boxes and 深度学习 server.), SOPHGO provides a complete developer portfolio series, including SC5 single-processor board card and supporting I/O expansion dock. The SC5 single-processor development board is equipped with a BM1684 processor, which can support 38 channels of HD video hardware decoding, 2 channels of high-definition video encoding and more than 16 channels of video analysis. It also has a RESET button, UART debugging interface, and an active cooling fan with a large air volume. The development board can be adapted to the standard PC development and testing environment. Its device drivers and development test environment are consistent with SC5 (X) series accelerator cards and support simultaneous upgrades.
Educational research: 深度学习 laboratory, teaching experiment platform and visual classroom; Test & development: processor function verification, desktop-level development environment.
SOPHON SDK one-stop toolkit provides a series of software tools including the underlying driver environment, compiler and inference deployment tool. The easy-to-use and convenient toolkit covers the model optimization, efficient runtime support and other capabilities required for neural network inference. It provides easy-to-use and efficient full-stack solutions for the development and deployment of deep learning applications. SOPHON SDK minimizes the development cycle and cost of algorithms and software. Users can quickly deploy deep learning algorithms on various deep learning hardware products of SOPHGO to facilitate intelligent applications.
deep learning Developer Portfolio
deep learning computing accelerator card
deep learning computing accelerator card
TPU core architecture
SOPHON
SOPHON
SOPHON
SOPHON
NPU core number
64
-
64
192
deep learning performance
FP32(FLOPS)
2.2T
-
2.2T
6.6T
INT8(OPS) Winograd OFF
17.6T
-
17.6T
52.8T
INT8(OPS) Winograd ON
35.2T
-
35.2T
105.6T
Processor
ARM 8-core A53 @ 2.3GHz
-
ARM 8-core A53 @ 2.3GHz
3x ARM 8-core A53 @ 2.3GHz
VPU
Video decoding capability
H.264:1080P @960fps
H.265:1080P @960fps
-
H.264:1080P @960fps
H.265:1080P @960fps
H.264:1080P @2880fps
H.265:1080P @2880fps
Video decoding resolution
CIF / D1 / 720P / 1080P / 4K(3840×2160) / 8K(8192×4096)
-
CIF / D1 / 720P / 1080P / 4K(3840×2160) / 8K(8192×4096)
CIF / D1 / 720P / 1080P / 4K(3840×2160) / 8K(8192×4096)
Video encoding capability
H.264:1080P @50fps
H.265:1080P @50fps
-
H.264:1080P @50fps
H.265:1080P @50fps
H.264:1080P @150fps
H.265:1080P @150fps
Video encoding resolution
CIF / D1 / 720P / 1080P / 4K(3840×2160)
-
CIF / D1 / 720P / 1080P / 4K(3840×2160)
CIF / D1 / 720P / 1080P / 4K(3840×2160)
Video transcoding capability (1080P to CIF)
Max. 18 channels
-
Max. 18 channels
Max. 54 channels
JPU
JPEG image decoding capability
480 images / second @ 1080p
-
480 images / second @ 1080p
1440 images / second @ 1080p
Maximum resolution (pixels)
32768×32768
-
32768×32768
32768×32768
System interface
Data link
EP PCIE X8
RC PCIE X8
PCIE X2
PCIE X16
PCIE X8
Operating mode
EP+RC
SOC extension
EP
EP
Physical / power interface
PCIE X16
12VDC Jack
PCIE X16
PCIE X16
RAM
Standard configuration
12GB
-
12GB
36GB
Maximum capacity
16GB
-
16GB
48GB
Power consumption
30W MAX
No load: 6W
With load: 30W
30W MAX
75W MAX
Heat dissipation mode
active
-
active
passive
Working status display
N/A
LED x3 (power / hard disk / status)
LED x1
LED x1
External I/O expansion *
SD-Card
1
-
-
RESET Button
1
-
-
RJ45
2 *1000Base-T
-
-
USB
4
-
-
SATA
1
-
-
4G/LTE
1
-
-
micro USB
1
-
-
working temperature
0℃-55℃
-10℃-55℃
0℃-55℃
Deep learning framework
Caffe / TensorFlow / Pytorch / Mxnet / Darknet / Paddle
Operating system support
Ubuntu / CentOS / Debian
compatibility
Compatible with mainstream x86 architecture and ARM architecture servers
Localization support
Support domestic RISC-V system such as Feiteng, Shenwei, Zhaoxin, etc.; support domestic Linux operating system such as Kylin, Deepin, etc.; support domestic deep learning framework Paddle Lite
Length x height x width (including bracket)
200x111.2x19.8mm
206x28.5x59.5mm
169.1x68.9x19mm
169.1x68.9x19.5mm
* All external I/O expansion interfaces in the deep learning developer portfolio must be used with SC5-IO