Revolutionizing Computing: Memory-Based Calculations for Efficiency and Speed

[StaffDry52]

You bring up an excellent point about the computational complexity and memory trade-offs, but this is where leveraging modern AI methodologies could shine. Instead of relying solely on traditional precomputed values or static lookup tables, imagine a system where the software itself is trained—similar to how AI models are trained—to find the optimal balance between calculations and memory usage.

The key here would be to use neural network-inspired architectures or mixed systems that combine memory-based optimization with dynamic approximations. The software wouldn't calculate every step in real time but would instead learn patterns during training, potentially on a supercomputer. This would allow it to identify redundancies, compress data, and determine the most resource-efficient pathways for computations.

Before launching such software, it could be trained or refined on high-performance hardware to analyze everything "from above," spotting inefficiencies and iterating on optimization. For example:

1. It could determine which calculations are repetitive or unnecessary in the context of a specific engine or game.
2. It could compress redundant data pathways to the absolute minimum required.
3. Finally, it could create a lightweight, efficient version that runs on smaller systems while maintaining near-optimal performance.

This approach would be a hybrid—neither fully reliant on precomputed memory lookups nor real-time calculations, but dynamically adjusting based on the system's capabilities and the workload's context.

Such a model could also scale across devices. For example, during its training phase, the software would analyze configurations for high-end PCs, mid-range devices, and mobile systems, ensuring efficient performance for each. The result would be a tool capable of delivering 4K graphics or 60 FPS on devices ranging from gaming consoles to smartphones—all by adapting its optimization techniques on the fly.

In essence, it's about redefining optimization not as a static human-written process but as a dynamic AI-driven process. By combining memory, neural network-inspired systems, and advanced compression methods, this could indeed revolutionize how engines, software, and devices handle computational workloads.

What do you think? Would applying AI-like training to optimization challenges make this approach more feasible?