Edge AI Model Compatibility Explained

The EP-200Q supports AI models built with TensorFlow, PyTorch, and ONNX, optimized for CPU, GPU, and NPU inference using INT8 and INT16 quantization for edge deployment.

The era of on-device intelligence is here, and choosing the right edge AI platform can make or break your product’s success.

At its core, the EP-200Q is a compact, high-performance edge AI System-on-Module (SoM) powered by Qualcomm® Dragonwing's™ QCS6490. With advanced CPU, GPU, and dedicated neural processing hardware, it’s designed to run real-world AI workloads right where your data is created, on the device itself.

In this post, we break down which AI models the EP-200Q can support, and how that translates into better performance, lower latency, and real value for developers and product teams building smart devices.

AI Inference on the EP-200Q. What Works and Why It Matters

The EP-200Q isn’t just another embedded board, it’s built from the ground up to handle on-device AI inference efficiently. That means letting your application make intelligent decisions without sending data to the cloud. Let’s explore how the EP-200Q tackles AI workloads:

Multiple Inference Engines: CPU, GPU, and NPU

The EP-200Q can run AI models using:

1. CPU: flexible, supports many frameworks, but best for smaller models

2. GPU: great for parallel computation, good balance for vision-centric workloads

3. NPU (Neural Processing Unit) : fastest and most efficient for optimized inference

For most AI use cases, especially vision AI and real-time sensing, leaning on the NPU delivers the best combination of speed and power efficiency.

Note: INT8/INT16 quantized models run faster and more efficiently on the NPU, ideal for production-grade AI inference.

This means you can take well-known AI frameworks like TensorFlow, PyTorch, or ONNX, convert them into optimized formats, and run them directly on the EP-200Q using industry-standard runtimes like SNPE, QNN, or TensorFlow Lite.

What This Enables in the Real World

Whether your product is a smart camera, robot, medical monitoring device, or industrial sensor, the EP-200Q supports the kinds of models that matter most:

• Computer vision and object detection
• Pose estimation and activity recognition
• Anomaly detection in machines and sensors
• Lightweight language models for commands and control
• Quantized CNNs optimized for edge inference

Because the EP-200Q can directly run these models locally, without constant cloud connectivity, you benefit from:

• Lower latency
• Improved privacy and data security
• Reduced bandwidth costs
• Offline operation reliability

All of this results in solutions that feel fast, smart, and robust in real environments.

How to Get Started Quickly

Don’t start from scratch, the EP-200Q ecosystem includes tools and workflows to streamline your development:

Model conversion tools: Easily convert from ONNX, TensorFlow, or PyTorch into deployable formats.

Docker-based build environment: Get up and running quickly without complex setup.

Qualcomm AI Hub support: Optional workflow to deliver optimized models with Qualcomm tools.

If you have specific models or frameworks you’re curious about, let’s talk,  the Silex team can help you map your workload to the right runtime.

Why It Matters for Your Product

Edge AI isn’t just a buzzword. It’s what enables:

• Real-time insights where it matters most
• Private and secure decision making
• Power-efficient inference for battery-powered devices
• Flexible support for popular AI frameworks

This translates into smarter, more reliable products, whether you’re building:

• AI-powered cameras that filter alerts at the edge
• Robots that navigate environments autonomously
• Industrial units that detect anomalies instantly
• Healthcare devices that monitor patients without cloud dependence

Ready to Try It for Yourself?

If you’re building an AI-enabled product and want to explore how the EP-200Q can accelerate your development, get early access to the EP-200Q platform now.