Current Problem

Problem Definition

Problem Definition

Shortage of GPU Computing Supply

The proliferation of artificial intelligence (AI) has brought attention to the issue of inadequate GPU computing supply. The main causes of computing supply issues related to AI in industrial development are as follows:

• Increased computing demands: AI technology requires a significant amount of computing resources and processing power. As AI models grow larger and the amount of data required for training and inference tasks increases, the demand for computing resources is rapidly escalating. Existing computing infrastructure is unable to adequately meet the demand.

• Hardware limitations: AI tasks require high-performance computing resources. However, such resources are limited, and optimizing them for AI tasks can be challenging with current computing technology. In particular, conventional central processing units (CPUs) alone have limitations for tasks that require parallel processing capabilities.

• Data-intensive operations: Training and inference of AI models are based on large amounts of data. These data-intensive operations involve numerous input/output tasks and computations. Operations such as data transfer, storage, and processing can impose a burden on computing resources, leading to supply shortages.

Increasing Computational Complexity of AI Systems

As machine learning and deep learning systems advance, the computational complexity of AI systems is increasing in various aspects. This problem can manifest in the following forms:

• Computational complexity: AI systems need to process and analyze massive amounts of data. These computational tasks require substantial resources and time, and real-time responsiveness may be necessary in some cases.

• Model complexity: AI models have intricate structures and require significant computational power for training and execution. In particular, deep learning models consist of numerous layers and parameters, further increasing computational complexity.

• Algorithm selection: The choice of algorithm can affect the computational complexity. Some algorithms may have high computational complexity, and if not optimized, additional computational costs can arise.