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Intelligent Accelerator Card SC5H

SC5H is equipped with intelligent vision deep learning processor BM1684, which can be integrated into servers and industrial computers, efficiently adapt to all deep learning algorithms in the market, and realize applications such as video structuring, face recognition, behavior analysis, state monitoring, and empower smart cities, smart transportation, smart energy, smart finance, smart telecommunications, smart industries and other fields

Support intelligent analysis of 16-channel HD video

High performance/power ratio, for edge-side deep learning applications.

Support multiple precision calculations such as FP32 and INT8

32 channels full HD video hard decoding capability, applicable to high-speed high-frame rate industrial cameras

2-channel HD video hard-coding capability, supporting 4K level semi-real-time encoding output

Video and picture decoding resolution range up to above 8K, suitable for all kinds of ultra-high-definition network cameras

Adapt to local workstation environments and domestic RISC-V systems such as Phytium and Shenwei.

Can be together used with SC5+ and accelerator cards and graphic cards of other brands to build a heterogeneous computing platform

Wide Application and Scenarios

SC5H is mainly applied in distributed deep learning computing and analysis scenarios on the edge side, such as traffic, urban management, communities and industrial inspection that require front-deep learning computing power; it can also be together used with other cards such as SC5 + on the same computing platform.

Easy-to-use, Convenient and Efficient

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.

Support mainstream programming framework

More

Performance Parameter

Deep learning developer portfolio

Intelligent Accelerator Card SC5H

Intelligent Accelerator Card SC5+

TPU core architecture

SOPHON

SOPHON

SOPHON

SOPHON

NPU core number

64

-

64

192

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 processor systems such as Feiteng, Shenwei, Godson, Haiguang, etc. Support domestic Linux operating systems such as Kirin and UOS; Support domestic frameworks Paddle Lite, Tengine, etc.

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