Hailo-8 M.2 AI Accelerator Module Based On the 26TOPS Hailo-8 AI Processor Suitable For Raspberry Pi 5

Hailo-8 M.2 AI Accelerator Module Based On the 26TOPS Hailo-8 AI Processor Suitable For Raspberry Pi 5

$1,166.76

$1,166.76 $0.00

Description

SPECIFICATIONS

Accessory Type: USB To DC Cable

Battery Included: no

Brand Name: UNISTORM

Compatible With: Raspberry Pi 5

High-concerned chemical: none

Origin: Mainland China

Typical Application Fields: Communication Hardware

Hailo-8 M.2 AI Accelerator Module,
based on the 26TOPS Hailo-8 AI processor, Supports Linux/Windows
Systems, Optional for PCIe To M.2 Adapter Board, Suitable For Raspberry
Pi 5

Hailo-8 AI Kit

Equipped With 26TOPS Hailo-8 M.2 AI Accelerator Module

This AI kit is launched by Waveshare to
provide a more cost-effective and high-performance AI solution for the
Raspberry Pi 5, optional for PCIe To M.2 adapter, suitable for
applications such as process control, safety, home automation and
robotics, etc.

Features At A Glance

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 processingof 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
PCIe To M.2 adapter
- Onboard power monitoring chip and EEPROM, supports real-time monitoring of device power status for more stable operation
- Raspberry Pi HAT+ compliant
- Reserved airflow vent, supports installing cooling fan for better heat dissipation of the AI module to improve performance
- Immersion gold process design, anti-oxidation and more durable

Hailo-8 AI M.2 Module Parameters

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 to22×42mmand 22×60mm

Equipped with Hailo-8 AI Accelerator to Step Up Your Edge Product Performance

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.

Hailo-8 performance parameters

NN Model	Input Resolution	mAP	Hailo-8L FPS (batch8)
yolov4_tiny	yolov4_tiny	18.98	610
yolov6n	yolov6n	34.3	345
yolov7	yolov7	49.8	45
yolox_s_wide	yolox_s_wide	42.4	75
yolov3	yolov3	38	26
yolov8n	yolov8n	37.23	270
yolov8s	yolov8s	44.75	128
yolov8m	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
Object Detection	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

Connecting to PI5 via 16PIN Cable

Based on 16PIN PCIe Interface of Raspberry Pi 5

tailored for Raspberry Pi 5

Standard Raspberry Pi 40PIN Header, Comes
with 2*20 Pin header for Stacking with Other HATs. Compact Size, More
Space-Saving, supports installing cooling fan

Reserved airflow vent for Cooling Fan

can be used together with the Pi5 Active Cooler Bto achieve better heat dissipation effect For the Pi5 And AI
Accelerator Module, keeping it cool even under heavy processing and
maximizing the Module Performance