As the automotive industry accelerates toward fully autonomous and software-defined vehicles (SDVs), the cost and complexity of software validation have become a central challenge. At aiMotive, we’re tackling this head-on with a GPU-accelerated, bit-accurate functional model of our aiWare NPU—designed to dramatically scale up Software-in-the-Loop (SiL) testing.

Why Software Validation is the Bottleneck in SDV Development

In SDVs, software is not just a component—it’s the product. With over-the-air (OTA) updates and continuous feature evolution, software validation must keep pace with rapid development cycles. This is especially true for AI-driven features in ADAS and autonomous driving (AD), where validation equals AI validation.

Traditional validation methods are too slow and expensive. Testing must simulate thousands of miles per hour of driving to ensure safety and performance. And because AI models are trained on powerful cloud GPUs but deployed on edge NPUs in vehicles, functional differences introduced during optimization (e.g., quantization, pruning) must be validated thoroughly.

The Role of aiWare in Accelerated SiL Testing

aiWare is aiMotive’s proprietary NPU IP, optimized for automotive-grade AI inference. To ensure that AI models behave consistently from cloud training to in-vehicle deployment, we developed a **GPU-accelerated, bit-accurate aiWare functional model**. This model enables:

• Real-time SiL testing on standard GPUs (e.g., NVIDIA RTX 40 series)
• Massive scalability using cloud or on-premise compute
• High fidelity emulation of aiWare inference behavior
• Cost-effective reuse of existing GPU infrastructure

This approach allows developers to validate AI functionality early in the development cycle, ensuring that results are portable to later stages like Hardware-in-the-Loop (HiL) and vehicle testing.

A Multi-Staged Testing Pipeline for Scalable Validation

aiMotive’s testing strategy follows a four-stage pipeline:

1. NN Inference Testing – Early validation of neural networks using emulated hardware.
2. SiL Testing – Application-level functional testing using raw sensor inputs.
3. HiL Testing – Full-stack validation on target hardware.
4. Vehicle Testing – Final validation in real-world conditions.

The aiWare SiL emulator plays a critical role in Stage 2, enabling rapid iteration and functional KPI evaluation without the need for dedicated hardware.

Multi-staged test pipeline is key - Using the right level of abstraction saves time and cost

Why GPU Acceleration Matters

Unlike traditional CPU-based emulators, our GPU-accelerated model achieves near real-time performance, making it ideal for large-scale regression testing. It supports:

• Bit-accurate emulation of aiWare inference
• Configurable execution for different NN architectures
• Seamless integration into CI/CD pipelines

This means developers can test more, faster, and with greater confidence—without waiting for hardware availability or incurring high infrastructure costs.

Conclusion: A Smarter Path to Affordable SDVs

Delivering affordable SDVs requires smarter, faster, and more scalable software validation. aiMotive’s aiWare SiL emulator is a key enabler in this journey, helping OEMs and Tier 1s reduce costs, accelerate time-to-market, and ensure functional safety across diverse platforms.
Whether deployed on-prem or in the cloud, this tool is indispensable for modern ADAS and AD development.