Giá chưa bao gồm thuế giá trị gia tăng (VAT) 10%
26TOPS Hailo-8 M.2 AI Accelerator Module
Sản phẩm nhập khẩu chính hãng
Hỗ trợ kỹ thuật trong quá trình sử dụng
Hailo-8 M.2 AI Accelerator Module, based on the 26TOPS Hailo-8 AI processor, suitable For Raspberry Pi 5
Hailo-8
The Hailo-8 AI M.2 module only
Hailo-8 AI M.2 module
- Powered by 26 Tera-Operations Per Second (TOPS) Hailo-8 AI Processor
- 2.5W typical power consumption
- Scalable, enabling simultaneous processing of multi-streams & multi-models
- Enabling real-time, low latency and high-efficiency AI inferencing on the edge devices
- Supports TensorFlow, TensorFlow Lite, ONNX, Keras, Pytorch frameworks
- Supports Linux and Windows
- Supports the temperature range of -40°C to 85°C
AI performance | 26 TOPS |
---|---|
Form Factor | M.2 Key M |
Power supply | 3.3V ± 5% |
Power consumption | 2.5W (Typ.) 8.65W (Max.) |
Interface | PCIe Gen3, 4-lane |
Certificate | CE, FCC Class A |
Storage temperature | -40 ~ 85°C |
Operating temperature | -40 ~ 85°C |
Operating humidity | 5% ~ 90%RH (no frosting) |
Dimensions | 22×80mm with breakable extensions to 22×42mm and 22×60mm |
The Hailo-8 M.2 module is an AI accelerator module for AI applications, based on the 26 tera-operations per second (TOPS) Hailo-8 AI processor with high power efficiency. The M.2 AI accelerator features a full PCIe Gen-3.0 4-lane interface, delivering unprecedented AI performance for edge devices.
The M.2 module can be plugged into an existing edge device with M.2 socket to provide low-power deep neural network inferencing. Leveraging Hailo's comprehensive Dataflow Compiler and its support for standard AI frameworks, customers can easily port their Neural Network models to the Hailo-8 and introduce high-performance AI products to the market quickly.
NN Model | mAP | Hailo-8L FPS (batch8) |
---|---|---|
yolov4_tiny | 18.98 | 610 |
yolov6n | 34.3 | 345 |
yolov7 | 49.8 | 45 |
yolox_s_wide | 42.4 | 75 |
yolov3 | 38 | 26 |
yolov8n | 37.23 | 270 |
yolov8s | 44.75 | 128 |
yolov8m | 50.08 | 55 |
Type | NN Model | Input Resolution | FPS | Power(W) | FPS/W |
---|---|---|---|---|---|
Classification | ResNet-50 v1 | 224x224 | 1332 | 3.45 | 386 |
MobileNet_v2_1.0 | 224x224 | 2444 | 2.152 | 1135 | |
EfficientNet_M | 240x240 | 889 | 3.5 | 254 | |
Object Detection | SSD_MobileNet_v1 | 300x300 | 1055 | 2.2 | 479 |
YOLOv5m | 640x640 | 218 | 4.6 | 47.3 | |
Segmentation | stdc1 | 1024x1920 | 54 | 2.9 | 18.6 |
Multi stream object detection (8 streams) | YOLOv3 | 608x608 | 69 | 4.9 | 14 |