Abstract. The paper describes one modeling automated hardware-software system to develop additive technologically -based products. Algorithm synthesis for industrial robot control was performed. In this case, the operating tool is an electron -beam projector with plasma emitter. The model testing results of thermal processes (in 3D-enqueuing) proceeding within additive substrate material system have been described. The modeling results revealed those required parameters of emission power which would provide the melting of additive material (TiC) excluding boiling.
The remarkable results of applying machine learning algorithms to complex tasks are well known. They open wide opportunities in natural language processing, image recognition, and predictive analysis. However, their use in low-power intelligent systems is restricted because of high computational complexity and memory requirements. This group includes a wide variety of devices, from smartphones and Internet of Things (IoT)smart sensors to unmanned aerial vehicles (UAVs), self-driving cars, and nodes of Edge Computing systems. All of these devices have severe limitations to their weight and power consumption. To apply neural networks in these systems efficiently, specialized hardware accelerators are used. However, hardware implementation of some neural network operations is a challenging task. Sigmoid activation is popular in the classification problem and is a notable example of such a complex operation because it uses division and exponentiation. The paper proposes efficient implementations of this activation for dynamically reconfigurable accelerators. Reconfigurable computing environments (RCE) allow achieving reconfigurability of accelerators. The paper shows the advantages of applying such accelerators in low-power systems, proposes the centralized and distributed hardware implementations of the sigmoid, presents comparisons with the results of other studies, and describes application of the proposed approaches to other activation functions. Timing simulations of the developed Verilog modules show low delay (14–18.5 ns) with acceptable accuracy (average absolute error is 0.004).
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