Ascend 310
16 TOPS INT8 edge AI processor with 8W power consumption, dual-core Da Vinci architecture for computer vision and edg...
Product Overview
Description
The Ascend 310 brings powerful AI capabilities to edge devices, enabling real-time inference for computer vision, robotics, and intelligent cameras. With industry-leading power efficiency, it allows AI deployment in power-constrained environments.
Product Series
Ascend
Primary Application
Smart cameras
Key Features
- 16 TOPS INT8 performance at 8W power
- Dual Da Vinci AI cores for efficient inference
- Multi-channel video processing
- PCIe and USB interfaces for flexible integration
- Comprehensive CANN software support
- Hardware security features
Specifications
| AI Performance | 16 TOPS INT8 / 8 TOPS FP16 |
|---|---|
| Architecture | Dual-core Da Vinci |
| Memory | LPDDR4X up to 8GB |
| Power | 8W typical |
| Process | 12nm |
| Video Decode | 16-channel 1080p H.264/H.265 |
| Video Encode | 8-channel 1080p H.264/H.265 |
| Interfaces | PCIe 3.0, USB 3.0, GbE |
| Temperature | 0°C to +70°C commercial |
| Security | Hardware encryption, TrustZone |
Applications
Smart cameras
Electronic system design
Edge AI gateways
Electronic system design
Industrial inspection
Industrial automation and control
Autonomous robots
Electronic system design
FAE Expert Insights
"The Ascend 310 is my top recommendation for edge AI deployments. The 2 TOPS/W efficiency is genuinely impressive - I've deployed these in smart camera systems where power and thermal constraints were critical. The multi-channel video support is a standout feature; you can process 16 camera feeds simultaneously for applications like retail analytics or perimeter security. The CANN software stack has matured significantly, and model conversion from TensorFlow is now straightforward. For industrial applications, the commercial temperature range is adequate, but verify your ambient conditions. I recommend the development kit for initial evaluation."
Exceptional power efficiency with multi-channel video processing for edge AI
— David Wang, BeiLuo
Frequently Asked Questions
What is the maximum video processing capability of Ascend 310?
The Ascend 310 supports impressive video processing capabilities: 16-channel 1080p H.264/H.265 video decoding at 30fps per channel, and 8-channel 1080p video encoding. This enables processing of multi-camera feeds simultaneously for applications like video surveillance, retail analytics, and traffic monitoring. The video processing engines work in parallel with the AI cores, allowing real-time inference on all video streams without frame drops. For higher resolutions, the Ascend 310 can handle 4-channel 4K video decoding or 2-channel 4K encoding.
Design your camera system within these limits, or contact us for solutions requiring higher channel counts.
How does Ascend 310 compare to GPU-based AI accelerators?
Compared to GPU-based accelerators, Ascend 310 offers several advantages: 1) Higher TOPS-per-watt efficiency - 2 TOPS/W vs. typically 0.5-1 TOPS/W for GPUs; 2) Lower cost - optimized for inference without GPU overhead; 3) Better deterministic latency - designed for real-time applications; 4) Lower thermal requirements - passive cooling possible; 5) Purpose-built AI architecture - Da Vinci cores optimized for neural networks vs. general-purpose GPU. However, GPUs offer broader software ecosystem (CUDA) and may be preferable for development flexibility or applications requiring both AI and graphics processing.
Choose Ascend 310 for power-efficient, cost-optimized inference deployments; choose GPUs for development flexibility or graphics+AI workloads.
What is the typical latency for inference on Ascend 310?
Ascend 310 delivers low-latency inference suitable for real-time applications: Single image classification (ResNet-50) typically completes in 2-4ms; Object detection (YOLOv3) processes in 10-20ms per frame; Face detection runs at sub-10ms per frame. These latencies enable real-time processing at 30-60fps for most computer vision applications. The deterministic architecture ensures consistent latency, critical for time-sensitive applications like autonomous systems. Actual latency depends on model complexity, input resolution, and batch size.
Benchmark your specific model on the Ascend 310 development kit to verify latency meets your application requirements.
Does Ascend 310 support model quantization?
Yes, Ascend 310 supports INT8 quantization for optimal performance. The CANN software stack provides automatic quantization tools that convert FP32 models to INT8 with minimal accuracy loss. Quantization typically achieves 2x performance improvement with less than 1% accuracy degradation for most computer vision models. The Ascend 310 also supports FP16 for applications requiring higher precision. Mixed precision is supported, allowing different layers to use different precision levels. The quantization-aware training feature in MindSpore can further improve quantized model accuracy.
Use INT8 quantization for production deployment to maximize performance; use FP16 if accuracy requirements demand higher precision.
What thermal solution is required for Ascend 310?
The Ascend 310's 8W power consumption allows flexible thermal solutions: Passive cooling with a heatsink is sufficient for most applications with adequate airflow; Fanless designs are achievable in well-ventilated enclosures; Active cooling with a small fan enables higher ambient temperatures. The processor includes thermal monitoring and throttling protection. For industrial applications, ensure the junction temperature stays below 105°C. The compact thermal profile makes Ascend 310 suitable for space-constrained installations where larger GPUs won't fit.
Design your thermal solution based on ambient conditions and enclosure constraints. Contact our FAE team for thermal modeling assistance.