The article discusses the implementation of a hardware neuroaccelerator based on FPGAs of the Cyclone IV series with 115,000 logical elements. An assessment of the requirements for the hardware resources of the computing platform is given. The features of the implementation of the neural network in the tasks of cognitive robotics and industrial production have been investigated in order to improve safety in the interaction of a robot and a person.
The article describes the construction of a control system for mechatronic technological complexes, such as machine tools and robots. Like the human brain, the core of the system uses methods of parallelism of computational processes to increase productivity through the use of hardware computational and communication units in the processor architecture. In particular, a method has been developed to improve the performance of calculating the inverse kinematics problem for a five-axis machining centre with a globe rotary table. To improve the efficiency of electrical industrial equipment control and ensure safety, a modular system based on a ring interface and a real-time communication protocol was developed. The structure of the control system is synthesized by a memory-centric approach. Computing units in the processor architecture and external automation control modules are represented as memory areas. This ensures simplicity and speed of interaction with the core of the system.
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