Artificial Intelligence & Deep Learning

Computers for Artificial Intelligence - Deep Learning

Since 2018/2019, ICP Germany has been offering a complete series of industrial PCs for the implementation of deep learning applications and projects in the field of artificial intelligence. The main focus of the industrial hardware under the IEI brand is an integrated intelligent Deep Learning Software from Intel. The Deep Learning technology is based on so-called neural networks, which enable machines to learn artificial knowledge through training and interference, to refine it and to recognize new information and process it sensibly.

Artificial intelligence and deep learning are part of the ongoing digitalization that Internet giants like Facebook, Google and Co are already using and will continue to be used in the future industry, where humans and machines interact.

Artificial Intelligence product overview

AI-Advantages Your Advantages

The advantages of AI solutions are versatile:

  • Pre-installed software package consisting of openVINO™ Toolkit, Intel® Media SDK, Ubuntu 16.04 Desktop LTS operating system and Intel® System Studio 2018
  • Intel® Core™ i5 /i7 processors and Intel® HD Graphics for analysis of high-resolution image and video data as well as machine and sensor data for optimally matched hardware performance
  • Flexible scalable and expandable systems through PCIe x4 based acceleration cards equipped with VPU and FPGA technology
  • Fast, simplified development and deployment of software at the Edge through practical reference examples

How does deep learning work How does artificial intelligence work?

Deep learning (DL) is a machine learning technique that is based on deep neural networks and recurrent neural networks architectures. These artificial neural networks are comparable with the structure of the human brain. The underlying learning procedure is realized by using representations of features directly from data such as images, text and sound. These representations are the result of abstracting input data in multiple layers and on different levels, which form a concise network. Deep Learning is applied for instance in fields of computer and machine vision, speech and audio recognition or social network filtering. In some cases the performance of deep learning algorithms can be even more accurate than human judgement.

Artificial Intelligence overview

There are three steps to sucessfully conduct deep learning projects:

3 steps of Artificial Intelligence - 1. Data Acquiry width= 3 steps of Artificial Intelligence- 2. Training 3 steps of Artificial Intelligence - 3. Inference

AI Data acquisition AI-Training Data acquisition & training

Before the training of machine vision applications begins, data acquisition is required. A huge and varied set of data from any kind of source like web, cloud, sensors and images must be available. Then data aggregation and labeling takes place in order to be able to classify e.g. animal’s images accurately. Based on the aggregated data the Deep Learning model gets developed and trained. The training is being done by a learning procedure that consists of layers. The first input layer contains raw data e.g. a matrix of pixels which becomes automatically more abstract in the next layers (hidden). Step by step specific features (e.g. edges, eyes, nose) are encoded until the complete animal is recognized in the final output layer. The result is a dog can be distinguished from a cat.

Artificial Intelligence training system

GRAND-C422 AI Training System

The AI training system Grand-C422 is dedicated for these tasks because it offers a wide range of slots for storage expansion, acceleration cards and video capture, ThunderboltTM or PoE add-on cards for unlimited data acquisition possibilities. In order to develop a useful training model, existing and widely used deep learning training frameworks such as Caffe, Tensor-Flow or Apache MXNet are recommended. These facilitate the definition of the apt architecture and algorithms for a distinct AI application.

AI-Inference Inference & optimization

Is the Deep Learning model ready for trial, it can be transferred to the inference system TANK-870AI for performance optimization and the execution of inference tasks. Inference is known as reasoning in steps, whereby existing knowledge is used to draw logic conclusions. In the field of AI, neural networks undertake this “prediction” and “scoring” by passing new data through a trained model to compute results for each query.

Artificial Intelligence Inference Systems

TANK-AI Inference System

One major advantage of the TANK-870AI is the preinstalled open-source developer toolkit Open Visual Inference Neural Network Optimization (OpenVINOTM). It is compatible with widely adopted Deep Learning frameworks, optimizes the Deep Learning model via an integrated model optimizer and inference engine (runtime) and accelerates the deployment process of the inference solution to the edge by pre-trained models, samples, and demos.

Included software

Artificial Intelligence - OpenVINO Toolkit

AI-Computing-Accelerator Computing accelerators

In addition, the performance of optimized inference models can be further enhanced by adding heterogeneous low profile computing acceleration cards such as the Intel® field programming gate arrays (FPGA) or the Intel® Movidius® vision processing units (VPU). An alternative to the OpenVINOTM toolkit is TensorRT. The combination of GRAND-C422, TANK-870AI, the accelerator cards and a Deep Learning toolkit form IEI’s AI ready solution.

Artificial Intelligence Computing Accelerator Cards

IEI AI Ready Solution

ICP offers three different acceleration cards. Whereby the Mustang-V100-MX8 is based on Intel® Movidius Myriad X and the Mustang-F100-A10 is based on Intel® Arria 10GX 1150 FPGA. Both are designated for inference enhancement. The CPU acceleration card Mustang-200 combined two Intel® Core ULT CPUs and offers additional inference performance.

Intel® Vision Accelerator Design Products

Artificial Intelligence Computing Accelerator Cards

AI-Applications Applications


AI in traffic

Efficient road tolling and parking reduces fraud related to non-payment, makes charging effective, and reduces required manpower to process. Vehicle license plate analysis can be deployed on highways for electronic toll collection, and can be implemented as a method of cataloguing the movement of traffic as well as provide enhanced security by establishing data on suspicious vehicles in a more efficient way.


AI Face Detection

Behaviour monitoring helps to identify either suspicious behaviour or alerts local authorities if people are in need of medical support. Face analysis not only infers features of human like gender, age and facial expression. People counting based on AI prevents overcrowding of public places.


AI in Automation

During the manufacturing process, defects could be introduced and harmful to the quality. In order to classify defects with higher accuracy automated optical inspection is a worthwhile measure that can cut costs on review and repair stations.

Machine vision

AI in Machine Vision

Information technology adds intelligence to factories from design to the end of the process. Today’s technologies automate the collection, storage and retrieval of data from across multiple factories and factory sub-systems to make the data available for analysis. Agricultural products are valued by their appearance. Their color indicates parameters like ripeness, defects, etc. The quality decisions, however, vary among the graders and are often inconsistent. Machine vision technology offers the solution for all these problems.


AI in Retail

Facial recognition and object detection are two use cases that are of high potential in retail stores. Facial recognition systems can spot convicted or admitted shoplifters or recognizes trusted patrons that are offered specific bargain based on their past buying behavior, gender, age and mood.


AI in Medical Applications

In healthcare deep learning and AI could be used for diagnosis support and patient monitoring. As of today the evaluation of clinical patterns of the illness age-related macular degeneration and the classification of its degeneration stage has been proved very successful.

AI-News More news about AI

1. TANK-870AI – Industrial Inference System

Implementing AI in the production environment. Artificial intelligence (AI) is also gaining more and more importance in the industrial sector. A common application in the production environment is automatic optical inspection...

2. TANK-870AI – AIoT Developer Kit for inference at the edge

Deep Learning consists of the sub-areas training and inference. In the training phase, a training model is developed, tested and refined to the desired accuracy using a comprehensive dataset of images and videos...

3. Mustang-V100- Intel® Movidius™ Myriad™ X VPU accelerator card

The realization of deep learning inference (DL) at the edge requires a flexibly scalable solution that is power efficient and has low latency. At the edge mainly compact and passive cooled systems are used that make quick decisions without uploading data to the cloud...

4. Mustang-F100 – FPGA based AI-accelerator

The FPGA-based Mustang-F100 accelerator card from ICP Deutschland is designated for industrial inference systems. It is primarily used for deep learning inference in real time, for video and image processing as well as for the analysis of machine and sensor data...

5. GRAND-C422 – 19” PC System with Xeon® W Processor

Whether an AI training system or a high-performance system for image processing, rendering or mining, the requirements for performance, data storage capacity and transmission speed are almost exorbitant. The 19" PC...

6. Mustang-MPCIE-MX - Intel® MovidiusTM MyriadTM VPU accelerator

With the Mustang-MPCIE-MX2 card, ICP Deutschland expands its portfolio of KI accelerator cards with a Mini PCIe plug-in card variant. The Mini PCIe format enables system integrators to build small embedded PC systems with KI functionality for deep learning...

7. MUSTANG-V100 Series - Intel® MovidiusTM X VPU accellerator

With the Mustang-V100 series, ICP Deutschland offers a flexibly scalable solution for implementing Deep Learning Inference (DL) at the Edge, which is energy-saving and has a low latency time. The Edge primarily uses systems that are designed to make quick decisions without uploading to the cloud....

8. MUSTANG-M2BM-MX2 – Intel® MovidiusTM MyriadTM VPU accelerator

ICP Germany completes its portfolio of AI accelerator cards with the Mustang-M2BM-MX2 card. In addition to Mini PCIe and PCIe based solutions, an M.2 PCIe plug-in card variant is now available. With the M.2 format in the size 22x80 mm, system integrators are able to build small embedded PC systems with AI functionality as deep...

9. Ultra compact inference embedded PC with vision processing units

With the ultra compact ITG-100AI, ICP Germany offers an inference system that is prepared for use with dedicated neural network topologies (DNN). On the hardware side, the two Intel® Movidius™ Myriad™ X VPU in the ITG-100AI offer excellent inference performance per watt, 16 SHAVES cores for AI calculations...

Further information can be found in the following AI Info brochure (7,4MB) and our AI Flyer (2,4MB):
Artificial intelligence - Info Broschure Artificial intelligence - Info Flyer

AI-Videos Videos


Mustang-200: CPU accelerator

Mustang-200: CPU accelerator

Mustang-F100: FPGA accelerator

Mustang-F100: Installation guide

Medical AI Aplication

Mustang-V100: VPU accelerator

Mustang-V100: VPU accelerator