Research Algorithm for Neural Network Computing Based on Chip Acceleration

With the rapid development of artificial intelligence technology, neural networks have become a core component in machine learning and deep learning. Neural networks simulate the connections between neurons in the human brain and can process complex data for pattern recognition. However, to deploy and run neural networks in practical applications, it is necessary to rely on the support of high-performance chips. Therefore, the design of neural network architecture has a significant impact on chip design. The architecture of neural networks determines characteristics such as computational complexity, number of parameters, and memory usage, which directly affect key indicators such as power consumption, area, and performance of chips. In various practical application scenarios, deep convolutional neural networks have been widely used. However, trained model parameters are usually saved in floating-point format, which leads to high computational complexity, high memory consumption, and long inference time. Therefore, deploying high-precision models on edge devices with limited computing and memory resources becomes difficult. To address this issue, this study proposes an innovative dynamic pruning model algorithm. Unlike traditional neural network pruning algorithms that mainly prune fixed models after offline training, dynamic pruning algorithms prune by dynamically adjusting the network structure according to real-time requirements, which has a certain degree of innovation. This algorithm can automatically prune redundant connections and nodes based on the characteristics of the current task and the limitations of computing resources, thereby reducing computation and memory usage. Through dynamic pruning, high-precision models can be effectively deployed on edge devices, improving computational efficiency and memory utilization.