Edge AI Vision Solution

Application

Description

Complete edge AI solution combining Gowin FPGAs with optimized neural network accelerators for real-time image processing and object detection.

Core Advantages

Real-time Inference 30+ FPS processing with sub-50ms latency for time-critical applications
Low Power Under 5W total power consumption for edge deployment without cooling
High Accuracy 95%+ accuracy with optimized INT8 quantization matching GPU performance
Flexible Deployment Update AI models via firmware without hardware changes

Recommended Bill of Materials (BOM)

Item Part Number Description Quantity Datasheet
1 GW2A-LV55PG484C8/I7 Main FPGA for AI acceleration 1 📄 Download
2 MT41K256M16HA-125 4Gb DDR3 SDRAM for model storage 2 📄 Download
3 GW1N-LV9QN48C6/I5 Interface FPGA for camera input 1 📄 Download

Applications

Industrial defect inspection
Smart surveillance systems
Autonomous vehicle perception
Quality control automation
Robotic vision guidance

Technical Specifications

Inference Performance
30-60 FPS depending on model
Supported Resolutions
Up to 4K (3840x2160)
Power Consumption
<5W typical
Latency
16-33ms end-to-end
Model Format
TensorFlow/PyTorch convertible
Quantization
INT8 optimized
Camera Interfaces
MIPI CSI-2, DVP, GigE Vision

Customer Success Stories

Electronics Manufacturer

PCB Assembly |

Challenge

Needed real-time defect detection on production line with 99%+ accuracy and <100ms latency.

Solution

Deployed Edge AI Vision Solution with GW2A-LV55 implementing custom CNN for solder joint inspection.

Results

  • 99.2% defect detection accuracy
  • 50ms average processing latency
  • Zero false positives in production

Smart City Integrator

Security Systems |

Challenge

Required edge-based people counting and occupancy monitoring for privacy compliance.

Solution

Implemented distributed AI cameras using GW2A-LV18 with optimized MobileNet for person detection.

Results

  • 95% counting accuracy
  • Real-time processing at 30 FPS
  • <3W power consumption per camera

FAE Expert Insights

D

Dr. Sarah Chen

AI Solutions Architect

12+ years in embedded AI

Professional Insights

Based on extensive deployment experience, this Edge AI Vision Solution addresses the key challenges of real-time inference: latency, power, and accuracy. The FPGA-based approach provides unique advantages for industrial applications.

Key Takeaways

  • Model quantization is critical for FPGA efficiency
  • Pipeline parallelism reduces latency
  • INT8 inference achieves excellent accuracy
  • FPGA offers deterministic timing vs GPUs

Decision Framework

Decision Framework for Edge AI
Steps:
  1. Define accuracy and latency requirements
  2. Select appropriate CNN architecture
  3. Quantize and optimize model
  4. Prototype on development board
  5. Validate in target environment

Ready to Implement This Solution?

Contact our FAE team for design support and quotes

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Frequently Asked Questions

What neural network architectures are supported?

The Edge AI Vision Solution supports major CNN architectures including MobileNet V1/V2/V3 (optimized for edge), ResNet-18/34/50, SSD for object detection, YOLO-Tiny for real-time detection, and custom architectures. Gowin provides pre-optimized IP cores for these networks. For custom models, the Gowin AI Compiler converts TensorFlow/PyTorch models to FPGA implementations. Supported layers: Conv2D, DepthwiseConv, Fully Connected, ReLU, BatchNorm, MaxPool, AvgPool, Softmax. Contact FAE for specific architecture support.

MobileNet for efficiency, ResNet for accuracy, SSD/YOLO for detection. Contact FAE for architecture selection guidance.

What frame rates and resolutions are achievable?

Performance depends on FPGA device and model complexity: GW2A-LV18: 720p@60 FPS with MobileNet, 1080p@30 FPS with optimized models

GW2A-LV55: 1080p@60 FPS or 4K@30 FPS with larger models. Object detection (SSD) typically achieves 30 FPS at 720p. Latency ranges from 16ms (single frame) to 33ms (pipeline). For multi-camera applications, the solution can process 4x 720p streams simultaneously on GW2A-LV55. Contact FAE for performance estimation based on your specific model.

Select FPGA based on resolution and frame rate requirements. LV18 for 720p, LV55 for 1080p/4K.