Fanless Edge system for neural networks

The built-in eBox560-900 system is Axiomtek’s response to the growing popularity of graphics processing units (GPUs), which have a parallel structure and can handle a large amount of simple computational operations at the same time.

Develop applications with ease

Axiomtek takes precedence in applications where the number of simultaneous computational operations is too large and at the same time they are too monotonic for the central processing unit (CPU).

In addition to the gaming world, rendering and non-negligible mining operations, the need for parallel computing is present in artificial intelligence (AI) systems, especially in machine deep learning. The latter areas of artificial intelligence deal with neural networks. You can read more about neural networks at the bottom of the page.

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Use a robust embedded system

The compact and robust design of the eBOX560-900, compliant with IP40 protection, operates in the extended temperature range from -10 °C to 50 °C and is resistant to vibration up to 3G, and complies with CE and FCC Class A certification. DIN rail or wall mounting.

The computer has USB, two Ethernet and HDMI output that supports 4 K resolution. There is room for a PCIE Mini expansion card, for WLAN and Bluetooth or a 3G/4G/LTE/GPRS module, which provides wireless connectivity via the option of connecting four external antennas.

Exploit amazing development tools

In terms of software, the computer is supported by the Linux 16.04 operating system. The Jetson Development Pack (JetPack) SDK is available to developers , which contains all the necessary tools for application development. JetPack includes machine learning tools, machine vision, GPU calculations, and multimedia.

JetPack tools:

  • Deep Learning: TensorRT, cuDNN, NVIDIA DIGITS™ Workflow
  • Computer Vision: NVIDIA VisionWorks, OpenCV
  • GPU Compute: NVIDIA CUDA, CUDA Libraries
  • Multimedia: ISP Support, Camera imaging, Video CODEC
Axiomtek eBox560-900-FL Network scheme

Teach your machines

It is common for people to learn how machines work and how to operate them. If the pieces from the production line do not meet the quality standard, the operator corrects the machine and tool parameters according to experience. Or if the car body part is not well painted, additional human work is required, which again, according to experience, corrects the defective work of the robot.

With Industry 4.0, where processes are digitized, AI does the machine management. As the concept of machine learning suggests, machines themselves understand processes and adjust their parameters according to their surroundings.

Check out the benefits

Defective body painting is recorded, data are collected at certain points of painting parameters, such as paint thickness, PH values ​​and drying time. Depending on the captured and surrounding parameters and the required final values, AI optimizes the painting process, improves the quality and does not require additional human correction.

Machine learning brings advantages mainly in improving the quality of the product and the flexibility of the production process. Data collected from the production process form the basis for analysis. This analysed data not only helps us to better understand the process, but also to optimize it. Data evaluation, on the other hand, results in constant process optimization with respect to current conditions.

Computer vision is one of the most important areas of artificial intelligence. This deals with computer systems capable of interpreting and analysing images. In the industrial environment, we find useful values ​​of computer vision in quality control in production processes and in the positioning of the piece for further processing.

Among other things, Tipteh offers solutions in the field of 3D machine vision, in collaboration with Photoneo 3D scanners, which also use the Jetson platform to implement algorithms for spatial perception of objects. A common use case is integration with a robot in the process of picking randomly positioned pieces.

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Axiomtek eBox560-900-FL Network scheme

Implement neural networks

Neural network is a computational model for parallel information processing, where the basic building blocks of the network are artificial neurons or threshold functions. Networks have multiple inputs and one or more outputs. The inputs are weighted differently and are connected to the output via one or more levels – one level is shown in the sketch.

The mathematical model of a neuron sums the signals (w n ) of differently weighted inputs (x n ). If the sum exceeds a certain threshold, the neuron sends a signal according to the activation function. If the network is multilevel, and usually is, the transmitted signal is received by another neuron in the next layer, which also processes it.

Meet the elements of a neural network

  • Synaptic weights: Wn values ​​are weights associated with a single neuron that determines the strength of the input vector Xn,
  • The neuron threshold is the value at which the output is activated,
  • Activation or a transfer function, which can be of various forms of functions (sigmoid in the figure), performs a mathematical operation on the output signal,
  • The grid output is the weighted sum of the inputs.

Teach the network how to react to a pattern

Neuron diagram

The weights of neurons and the threshold at which a neuron emits a signal are formed by learning. In learning mode, therefore, we teach the network how to react to a particular pattern of inputs. We use a large database of inputs for learning and determine the desired output. To get the desired result, the learning network searches for optimal weight values ​​and thresholds.

Operations with neural networks are performed in parallel, as each neuron operates relatively independently of the others, so neural networks are able to adapt to a complex environment, with many input parameters, in real time and with minimal delays.

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