Problem Statement

Problem Statement

Energy Consumption and Inefficiency in Traditional AI Systems

As artificial intelligence continues to expand across industries, the demand for computational resources grows exponentially. Traditional AI systems, powered by cloud-based infrastructure and centralized servers, are resource-intensive, requiring massive amounts of energy to process complex tasks. This not only leads to higher operational costs but also contributes to environmental concerns due to the significant carbon footprint associated with running these systems.

In addition, AI workloads often involve tasks such as model training and data processing that are too large for edge devices and inefficient when handled by traditional cloud computing. These tasks can experience latency issues, making real-time processing nearly impossible for applications like autonomous vehicles, drones, or robotics.

Centralization and Latency Challenges

Current AI solutions are often limited by centralization, leading to bottlenecks in processing power. Centralized cloud providers struggle to handle the growing volume of AI tasks, leading to high latency and slow response times. Real-time applications that require near-instantaneous decision-making, such as autonomous vehicles and IoT devices, are particularly affected by this.