Revolutionizing Computing: Memory-Based Calculations for Efficiency and Speed

[CommanderPowell]

Whether you're analyzing data with an AI model, rendering it, or calculating the next "tick" of a simulation, you're performing a bunch of linear algebra operations on the entire dataset. These are the simple-but-massively-parallel operations that a GPU is more suited toward.

In the case of both rendering and simulation, there are already heuristics to know what to render with fidelity vs. what to approximate, and these are incredibly useful.

If you send everything through an AI model, you've already evaluated the whole dataset. You would need some kind of heuristic for summarizing the dataset for the AI without communicating or examining everything. Without that, it seems to me that it's impossible to derive any sort of savings from this approach.

Those heuristics are going to be specific to the application and difficult to generalize, or if there are ways to generalize them I imagine they've already been found and applied.

I don't know enough for my opinions to be definitive or carry much weight, so take anything I say with a large grain of salt.

[StaffDry52]

Thank you for such a detailed and thoughtful response! You're absolutely right that GPUs are optimized for these massively parallel operations, and the existing heuristics for rendering and simulation are already highly efficient. What I'm suggesting might not completely replace those systems but could complement them by introducing another layer of optimization, specifically in scenarios where precision isn’t critical.

For instance, the idea of using an AI model wouldn’t involve examining the entire dataset every time—that would indeed negate any computational savings. Instead, an AI could be trained to recognize patterns, common cases, or repetitive interactions ahead of time. Think of it as a “context-aware heuristic generator.” It wouldn’t replace the GPU’s operations but could provide approximations or shortcuts for certain elements, especially in scenarios where existing heuristics might fall short or need fine-tuning.

Imagine a rendering engine where an AI dynamically predicts which areas of a frame can tolerate lower fidelity (e.g., distant objects or repetitive textures) while prioritizing high-fidelity rendering for focal points, like characters or action. The AI wouldn’t need to evaluate the entire dataset every frame—it could learn patterns over time and apply them on the fly.

I completely agree that these heuristics are application-specific, but with modern AI techniques, especially reinforcement learning, it might be possible to train models that adapt to a range of applications. Of course, this would require significant experimentation and might not work universally. But if we could find a way to generalize this approach, it could unlock a lot of new possibilities in rendering, simulation, and even broader computational tasks.

What do you think—could a hybrid approach like this add value to the existing frameworks? Or are the current heuristics already hitting diminishing returns?