AI Developer Product Portfolio

Dual-mode split desktop-level design, flexible and easy to use, simple to develop.

Dual-mode split desktop-level design, flexible and easy to use, simple to develop.

For developers who first use Sophon BM1684 third-generation AI chip series of computing hardware products (including SM5 edge modules, SC5 + accelerator cards, edge computing boxes, AI servers, etc.), Bitmain provides a complete developer portfolio series, including SC5 single chip board, and supporting I/O docking station. The SC5 single-chip development board is equipped with a BM1684 chip, 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 capabilities. It also has a RESET button, reserved UART debugging interface, and use A large air volume active cooling fan, 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.

Application scenarios

Educational research:artificial intelligence laboratory, AI teaching experiment platform, visual classroom;
test & development:chip function verification, desktop-level development environment.

Easy to use and convenient, full stack efficient

BMNNSDK (BITMAIN Neural Network SDK) one-stop toolkit provides a series of software tools such as the underlying driver environment, compiler, inference deployment tool and so on. Easy to use and convenient, covering the model optimization, efficient runtime support and other capabilities required for the neural network inference stage, providing easy-to-use and efficient full-stack solutions for deep learning application development and deployment. BMNNSDK minimizes the development cycle and cost of algorithms and software. Users can quickly deploy deep learning algorithms on various AI hardware products of Fortune Group to facilitate intelligent applications.

Support mainstream programming framework

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Specifications

AI Developer Portfolio

AI computing accelerator card

AI computing accelerator card

TPU core architecture

SOPHON

SOPHON

SOPHON

SOPHON

NPU core number

64

-

64

192

AI computing power

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

CPU

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 @1000fps

-

H.264:1080P @960fps
H.265:1080P @1000fps

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 @70fps
H.265:1080P @60fps

-

H.264:1080P @70fps
H.265:1080P @60fps

H.264:1080P @210fps
H.265:1080P @180fps

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

800 images / second @ 1080p

-

800 images / second @ 1080p

2400 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 CPU system such as Feiteng, Shenwei, Zhaoxin, etc.; support domestic Linux operating system such as Kylin, Deepin, etc.; support domestic AI 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 AI developer portfolio must be used with SC5-IO