Cellular Automata Simulation on FPGA for Training Neural Networks with Virtual World Imagery

TL;DR

This paper introduces a hybrid FPGA-based cellular automata simulation system integrated with a 3D game engine, designed to efficiently generate detailed virtual environments for neural network training in gaming applications.

Contribution

It presents a novel architecture combining FPGA-implemented cellular automata with a selective data transfer method to enhance simulation scale and detail for neural network training.

Findings

Efficient data transfer via 'locus of visibility' reduces bottleneck.

Enables large-scale, fine-grained simulations for neural network training.

Integrates hardware simulation with photorealistic rendering for gaming AI.

Abstract

We present ongoing work on a tool that consists of two parts: (i) A raw micro-level abstract world simulator with an interface to (ii) a 3D game engine, translator of raw abstract simulator data to photorealistic graphics. Part (i) implements a dedicated cellular automata (CA) on reconfigurable hardware (FPGA) and part (ii) interfaces with a deep learning framework for training neural networks. The bottleneck of such an architecture usually lies in the fact that transferring the state of the whole CA significantly slows down the simulation. We bypass this by sending only a small subset of the general state, which we call a 'locus of visibility', akin to a torchlight in a darkened 3D space, into the simulation. The torchlight concept exists in many games but these games generally only simulate what is in or near the locus. Our chosen architecture will enable us to simulate on a micro level outside the locus. This will give us the advantage of being able to create a larger and more fine-grained simulation which can be used to train neural networks for use in games.