Powered by SOPHON AI processor BM1684, this mainboard
can be configured with 12GB RAM. INT8 computing power is up to 17.6TOPS. It
supports mainstream frameworks and complete, easy-to-use toolchain, featuring low cost of algorithm migration. With various interfaces, it is
easy to integrate into various edge embedded products. It can be applied to various AI scenarios, such as visual computing, edge computing,
general computing power services, security surveillance, UAV etc.
This mainboard is powered by SOPHON AI processor BM1684, which is octa-core ARM
Cortex-A53, up to 2.3GHz clock speed and 12nm
lithography process. With up to 17.6Tops INT8 computing power or 2.2Tops FP32 high-precision computing power, it supports mainstream
programming frameworks, which can be widely used in artificial intelligence inference for cloud and edge applications.
Up to 32-channel 1080P H.264/H.265 video decoding is supported. It can process
and analyze more than 16-channel HD video at the same
time, meeting the needs of various AI application scenarios, such as face detection on video streaming, license plate recognition, etc.
Based on INT8 quantified Batch4 measured data, AIO-1684JD4 has higher throughput
and energy efficiency ratio than the mainstream
intelligent computing module platform in the industry, and has more advantages in performance.
It supports dual 1000Mbps Ethernet, 2.4GHz/5GHz dual-band WiFi, expandable 5G/4G LTE network, enabling higher-rate communication.
The BMNNSDK2 one-stop deep learning development toolkit provides a series of
software tools including the underlying driver environment,
compiler and inference deployment tool. It supports mainstream frameworks: Caffe/TF/PyTorch/Mxnet/Paddle, mainstream network model and
custom operator development, Docker containerization, and rapid deployment of algorithm applications.
With a complete software framework, Artificial Intelligence inference for cloud
and edge applications can be easily achieved. All of them accelerate
development of edge applications, such as face recognition, video structuring, abnormal alarm, equipment inspection, and situation prediction, etc.
With HDMI, mSATA, USB3.0, USB2.0, RS485 and RS232, this mainboard can be directly applied to AI edge computing products.
The mainboard can efficiently adapt to all AI
algorithms on the market and integrate into edge computing boxes, which promote development of
site, intelligent transportation, smart classes, unmanned supermarkets, security surveillance.
Integrated high-performance octa-core ARM A53, 12nm lithography process, clock speed up to 2.3GHz
Built-in tensor computing module TPU, computing power up to:
17.6T (INT8) / 2.2T(FP32) / 35.2 T(INT8, enable winograd)
TPU contains 64 NPU arithmetic units. Each NPU contains 16 EU arithmetic units, 1024 EU in total.
Support mainstream programming frameworks, such as TensorFlow / Caffe / PyTorch / Paddle /
ONNX / MXNet / Tengine / DarkNet
Up to 32-channel H.265/H.264 1080p@30fps video decoding
1080p@50fps video encoding
MJPEG image encoding and decoding up to 1080P@480fps
M.2 SATA3.0 SSD(2242) ×1
TF card slot ×1
1000Mbps Ethernet ×2
2.4GHz/5GHz dual-band WiFi, 802.11a/b/g/n/ac
expanded with 4G LTE (Mini PCIe interface), 5G (M.2)
HDMI1.4 audio output×1
M.2 SATA3.0 (expanded with 2242 SATA SSD) ×1
USB3.0 (current limit: 1A) ×2
USB2.0 (current limit: 500mA) ×2
149mm × 97mm × 37.45mm
Typical: 18W (12V/1500mA)
Max: 30W (12V/2500mA)
Operating Temperature: -20℃～60℃
Storage Temperature: -20℃～70℃
Operating humidity: 10%～90% (non-condensing)