On-Device Neural Network Inference in Interactive Simulations: Evaluating Unity Sentis Runtime Performance and Edge AI Integration (2026)

Abstract

This technical whitepaper provides an empirical evaluation and architectural benchmark of on-device deep learning inference within real-time interactive game simulations, focusing on the Unity Sentis framework and modern Edge AI paradigms (2026). Traditional cloud-based AI inference architectures introduce non-deterministic network latency, escalating per-token API costs, and privacy vulnerabilities that render them unsuitable for mission-critical game loops and responsive NPC behavioral state machines. This study investigates the hardware-accelerated local execution of Open Neural Network Exchange (ONNX) models directly on client endpoints across heterogeneous computing backends (DirectX 12, Vulkan, Metal, and WebGPU). Key engineering investigations include:1. Runtime latency profiling of tensor operations running on local client GPUs versus compute shaders.2. Memory footprint optimization and quantization techniques (FP32 to FP16 and INT8) to minimize frame-time variability and prevent garbage collection spikes.3. Decoupled asynchronous inference scheduling to maintain strict 60/120 FPS frame budgets during neural network forward passes.4. Practical deployment patterns for edge decision-making, procedural content synthesis, and real-time behavioral neural networks in modern interactive engines. For the comprehensive engineering tutorial, architectural code samples, and production workflows, access the full interactive publication on GameUnity:https://www.gameunity.store/2026/09/How-to-Integrate-AI-into-Game-Design.html