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Nebula Accelerator X3

Nebula Accelerator X3

High Cost Efficiency AI Accelerator
星空X3加速卡
四核CAISA引擎 四核CAISA引擎
Quad-Engine CAISA Accelerator
高算力性价比 高算力性价比
High Cost Efficiency
延时低至3ms 延时低至3ms
3ms Latency
Specifications
Chip:
CAISA
Power Supply:
PCIe
Architecture:
Custom AI Streaming Accelerator (CAISA) Architecture 3.0
Power Consumption:
TDP 56 W, Average Power Consumption 23.8 W
Chip Utilization Ratio:
Up to 95.4%
Cooling:
Active Cooling Fan
Peak Performance:
10.9 TOPS
Temperature:
-20℃ ~ 70℃
PCIe:
PCI Express3.0 x 8
Humidity:
<95%
Memory:
8 GB DDR4
Dimension:
169.5 mm x 69.6 mm (Standard half-length, half-height,single-slot)
Benchmark
ResNet-50

3.06 ms

1306.93 FPS

ResNet-152

8.68 ms

460.27 FPS

YOLOv3

31.06 ms

125.75 FPS

SSD-ResNet50

21.96 ms

182.16 FPS

U-Net Industrial

74.07 ms

54.01 FPS

* Batch=4, INT8, The above CNN models are created by TensorFlow framework.

* The result is from the DNN Benchmark evaluation project completed through China Academy of Information and Communications Technology(CAICT) entrusted by China Artificial Intelligence Industry Alliance(AIIA). http://www.aiiaorg.cn/benchmark/zh/top1.html?now=5


Benchmark Comparison
Left Slip
4.37x
92.3%
X3
21.1%
GPU
ResNet-50
4.35x
95.4%
X3
21.9%
GPU
ResNet-152
12.10x
82.4%
X3
6.8%
GPU
YOLOv3
77.1%
X3
GPU
SSD-ResNet50
65%
X3
GPU
U-Net Industrial
X3 VS GPU Chip Utilization Ratio
1.49x
1306.93
X3
879.00
GPU
ResNet-50
1.48x
460.27
X3
310.90
GPU
ResNet-152
4.12x
125.75
X3
30.50
GPU
YOLOv3
182.16
X3
GPU
SSD-ResNet50
54.01
X3
GPU
U-Net Industrial
X3 VS GPU Performance(FPS)
0.02x
3.06
X3
145.70
GPU
ResNet-50
0.02x
8.68
X3
411.66
GPU
ResNet-152
0.007x
31.60
X3
4190.86
GPU
YOLOv3
21.96
X3
GPU
SSD-ResNet50
74.07
X3
GPU
U-Net Industrial
X3 VS GPU Latency(ms)

Accelerate AI Deployment with Next-Gen Computing Platform

4009-606-596

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