The Problem with Current Computing Models

Limitations of Traditional Accelerators

Traditional computing models, predominantly powered by silicon-based GPUs and CPUs, face significant bottlenecks associated with data transfer speeds, memory limitations, and energy consumption. Current architectures are heavily reliant on slow electrical signals, which severely limit processing speeds and efficiency while also generating excess heat and requiring substantial energy resources. As AI models continue to grow in complexity, these limitations increasingly hinder computational potential, leading to an urgent need for innovative solutions.

Importance of Compute and Memory Capacity

The demand for higher compute and memory capacity arises from the exponential growth of data in various fields, including AI, big data analytics, and scientific simulations. Efficient processing of vast datasets with complex calculations is crucial for breakthroughs in medicine, climate science, and technology. Identifying a solution that transcends current capacities is essential for meeting the growing needs of commercial enterprises and research institutions.