With the rapid development of artificial technology, artificial neural networks (ANN) have come into people’s vision. Artificial neural network is a computing system that mimics the workings of the human brain. It is widely used in information recognition, natural language processing and predictive analysis. It uses electronic components to form nodes. These nodes are similar to human neurons: each node is connected with multiple nodes in adjacent levels with different weights. Very large scale integrated circuit (VLSI) technology plays a crucial role in optimizing the hardware implementation of ANN. This paper introduces the design principle of VLSI architecture for ANN. Using a simple ANN model- Perceptron as an example, the optimization design of tree adder and accumulator is discussed. In addition, several advanced design techniques and hardware-specific optimization strategies of VLSI technology in practical ANN applications will be discussed. Lastly, this paper summarizes the challenges that VLSI may face in the development of neural networks.
