Nexchip Dragon Series
Vendor: Nexchip
Category: Edge Computing / Autonomous Driving
Architecture: ARM + NPU
Introduction
Focus on high-end automotive chips. The flagship chip features a high-compute NPU designed specifically for AI inference tasks in smart cockpit and autonomous driving scenarios.
Specifications
| Model | Compute | Memory | Interface | TDP | Process |
|---|---|---|---|---|---|
| Dragon One | 8 TOPS (INT8) | 8GB LPDDR5 | Automotive-grade SoC | 15W | 7nm |
| Dragon Two | 16 TOPS (INT8) | 16GB LPDDR5 | Automotive-grade SoC | 20W | 7nm |
Official Website
OS Support
| Windows | Linux | macOS | Android |
|---|---|---|---|
| ❌ | ✅ | ❌ | ✅ |
Version History
| Version | Release Date | Description |
|---|---|---|
| Dragon One | 2023 | First automotive-grade AI chip, AEC-Q100 certified |
| Dragon Two | 2025 | NPU compute doubled, supports L4 autonomous driving |
Performance Benchmarks
| Model | Task | Performance Metric |
|---|---|---|
| Dragon One | Smart Cockpit Multimodal Interaction | 8 TOPS, multi-camera support |
| Dragon Two | Autonomous Driving BEV Perception | 16 TOPS, L4-level support |
Pricing
| Model | Reference Price | Notes |
|---|---|---|
| Dragon One | Contact vendor | Supplied to automakers (OEM) |
| Dragon Two | Contact vendor | In mass production 2025 |
Quick Installation
Linux (Automotive System)
# 1. Download Nexchip SDK (requires registered developer account)
# Visit https://www.nexchip.com.cn/ to obtain SDK
# 2. Set environment variables
export NEXCHIP_SDK_PATH=/opt/nexchip-sdk
# 3. Verify NPU
npu-info
Code Examples
C/C++ (Automotive NPU Inference)
#include <nexchip_npu.h>
// Initialize NPU inference engine
npu_handle_t handle;
npu_init(&handle, NPU_MODEL_AUTO);
// Run inference
npu_tensor_t input = npu_create_tensor(input_data, dims);
npu_tensor_t output = npu_infer(handle, input);
// Release resources
npu_release(handle);
Architecture Highlights
- ARM + NPU Heterogeneous: CPU and NPU deeply integrated, low-power real-time inference for automotive
- Automotive-Grade Certification: AEC-Q100 Grade 2 certified, meeting stringent automotive environmental requirements
- Unified Memory Architecture: CPU and NPU share LPDDR5, reducing data transfer overhead
Model Compatibility
| Model/Framework | Support | Notes |
|---|---|---|
| TensorFlow Lite | ✅ Native | Primary inference framework |
| ONNX Runtime | ✅ | Commonly used in automotive |
| PaddlePaddle Lite | ⚠️ | Under adaptation |
| Proprietary Quantization Toolchain | ✅ | INT8/INT4 quantization support |
Related Products
If you're evaluating alternatives, the following products may also fit your scenario:
- Huawei Ascend — Huawei (Cloud Training + Inference)
- Qualcomm Hexagon NPU — Qualcomm (Mobile NPU)
- Hailo-8 / Hailo-15 — Hailo (Edge AI Inference)
- AMD Ryzen AI NPU — AMD (NPU Neural Processor)
- Intel NPU (Neural Processing Unit) — Intel (NPU Neural Processor)
- Cambricon Siyuan 590 — Cambricon (ASIC Dedicated Accelerator)
- MediaTek NeuroPilot — MediaTek (NPU Neural Processor)