Product Information


MUSTANG-F100-A10-R10 electronic component of IEI

Accelerator Cards PCIe FPGA Highest Performance Accelerator Card with Arria 10 1150GX support DDR4 2400Hz 8GB, PCIe Gen3 x8 interface

Manufacturer: IEI
This product is classified as Large/Heavy, additional shipping charges may apply. A customer service representative may contact you after ordering to confirm exact shipping charges

Price (USD)

200: USD 1592.3954 ea
Line Total: USD 318479.08

0 - Global Stock
MOQ: 200  Multiples: 200
Pack Size: 200
Availability Price Quantity
0 - Global Stock

Ships to you between Mon. 09 Oct to Fri. 13 Oct

MOQ : 200
Multiples : 1
200 : USD 2249.9375
250 : USD 2229.2874
500 : USD 2208.5874
1000 : USD 2187.8625
2000 : USD 2167.1125
2500 : USD 2146.3125
3000 : USD 2125.4876
4000 : USD 2104.625
5000 : USD 2083.725
10000 : USD 2062.7875

0 - Global Stock

Ships to you between Fri. 13 Oct to Tue. 17 Oct

MOQ : 200
Multiples : 200
200 : USD 1641.3922

Product Category
Accelerator Cards
Interface Type
Minimum Operating Temperature
- 5 C
Maximum Operating Temperature
+ 60 C
169.5 Mm X 67.6 Mm X 33.7 Mm
2.4 KHz
Memory Type
Power Consumption
60 W
Lte Routers
Number Of Ports
1 Port
Product Type
Factory Pack Quantity :
Ethernet & Communication Modules
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An IntelVision Accelerator Design Product Intel Vision Accelerator Design with Intel Arria 10 FPGA 2019 Accelerate To The Future Powered by Open Visual Inference & Neural Network Optimization (OpenVINO) toolkit Ubuntu 16.04.3 LTS 64bit, CentOS 7.4 64bit (Windows & more OS are coming soon). Supports popular frameworks...such as TensorFlow, MxNet, and CAFFE. Easily deploy open source deep learning frameworks via Intel Deep Learning Deployment Toolkit . Provides optimized computer vision libraries to quick handle the computer vision tasks. Intel FPGA DL Acceleration Suite. A Perfect Choice for AI Deep Learning Inference Workloads OpenVINO toolkit Deep learning and inference Deep learning is part of the machine learning method. It allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.Deep neural network and recurrent neural network architectures have been used in applications such as object recognition, object detection, feature segmentation, text-to-speech, speech-to-text, translation, etc.In some cases the performance of deep learning algorithms can be even more accurate than human judgement. Al Sense, learn, reason, act, and adapt to the real world without explicit programming Perceptual Data Analytics Machine Learning Build a representation, Understanding Computational methods that use learning algorithms to build a model from data query, or model that Detect patterns (in supervised, unsupervised, semi-supervised, or reinforcement mode) enables descriptive, in audio or interactive, or predictive visual data analysis over any amount Deep Learning of diverse data Algorithms inspired by neural networks with multiple layers of neurons that learn successively complex representations Convolutional Neural Networks (CNN) DL topology particularly effective at image classification In the past, machine learning required researchers and domain experts knowledge to design filters that extracted the raw data into feature vectors. However, with the contributions of deep learning accelerators and algorithms, trained models can be applied to the raw data, which could be utilized to recognize new input data in inference. Learning from existing data Predict new input data Training Inference Training Dataset New Data Backward Trained Model Forward Forward DOG DOG CAT 1

Tariff Desc

8542.31.00 63 No Hybrid integrated circuits

Electronic integrated circuits: Processors and controllers, whether or not combined with memories, converters, logic circuits, amplifiers, clock and timing circuits, or other circuits
Monolithic integrated circuits, Digital:
IEI Technology