1. Cross-Platform Simulation and Hardware Abstraction (Performance Modeling)
Builds performance models for different Edge AI hardware targets (e.g., NPU, GPU, DSP), including constraints such as memory bandwidth, compute throughput, and latency. Enables realistic simulation of AI model execution on selected platforms without physical hardware. Supports configurable parameters (input resolution, batch size, quantization level) to accurately estimate FPS, latency, power consumption, and memory usage.

2. Model and System-Level Co-Simulation (End-to-End Pipeline)
Simulates not only individual models but the entire AI pipeline, including pre-processing, inference, and post-processing. Supports real-world application scenarios (e.g., ADAS, DMS, smart surveillance) to evaluate end-to-end latency, multi-model scheduling, and task contention. Helps identify bottlenecks early, such as I/O blocking or suboptimal NPU utilization.

3. Automated Deployment and Configuration Generation (Toolchain Integration)
After simulation, automatically generates deployment configurations tailored to the target platform, including model conversion, quantization, and runtime settings. Provides one-click deployment (CI/CD-like workflow) to seamlessly deploy AI models and system designs onto designated Edge AI devices. Compatible with mainstream toolchains such as TensorRT, TFLite, ONNX Runtime, and vendor-specific SDKs, reducing porting and optimization effort.