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Equipped with AI Accelerator for Realizing Edge AI: Introducing the Low-Power MCU MAX78000 Series
Analog Devices' MAX78000 series enables low-power AI (Artificial Intelligence) processing on edge devices, with the ability to handle applications such as machine vision, audio, and facial recognition in real time.
This series can power AI using less than 1/100th the power of other embedded solutions.
The MAX78000 series contains two microcontroller cores (Arm Cortex-M4 and RISC-V) and an accelerator dedicated to Convolutional Neural Networks (CNN). This resolves challenges in AI inference processing at the edge, such as power consumption and latency.

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MAX78000 Internal Block Diagram
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MAX78002 Internal Block Diagram
MAX78002 Features
Dual-core, low-power microcontroller
- Arm Cortex-M4 processor with FPU: up to 120MHz
- 2.5MB Flash, 64KB ROM, and 384KB SRAM
- Optimized performance with a 16KB instruction cache
- Optional Error Correction Code (ECC SEC-DED) for SRAM
- 32-bit RISC-V coprocessor: up to 60MHz
- Up to 60 general-purpose input/output pins
- MIPI Camera Serial Interface 2 (MIPI CSI-2) Controller V2.1: supports two data lanes
- 12-bit parallel camera interface
- I2S controller/target for digital audio interface
- Secure Digital Interface: supports SD 3.0 / SDIO 3.0 / eMMC 4.51
Convolutional Neural Network (CNN) accelerator
- Convolutional Neural Network (CNN) accelerator
- Supports 2 million 8-bit weights (1, 2, 4, and 8-bit weights)
- CNN data memory: 1.3MB
- Programmable input image size: up to 2048 × 2048 pixels
- Programmable network depth: up to 128 layers
- Programmable network channel width per layer: up to 1024 channels
- 1- and 2-dimensional convolution processing
- Able to process VGA images at 30 fps
Security and Integrity
- Secure boot available
- AES 128/192/256 hardware acceleration engine
- True Random Number Generator (TRNG) seed generator
Use Case 1: Gesture Sensor
A Gesture Solution Using MAX78000
- Deep learning-based gesture/palm shape determination
This configuration achieves high-speed processing using MAX78000's AI accelerator for determination.
Reference
Example Gesture Sensor System
Gesture / Shape Recognition Processing Time Comparison
Determination Processing H/W | Gesture (msec) | Shape Recognition (msec) |
---|---|---|
AI accelerator | 0.583 | 0.189 |
CortexM4(100MHz) | 11.3 | 3.28 |
- Results show over 20× faster processing speed than Cortex M4 cores.
Use Case 2: Face Recognition System
A Gesture Solution Using MAX78000
- Feature extraction method
Uses MTCNN [1] and FaceNet [2] algorithms for extracting facial features. - Post-processing
Estimates the model with the closest features based on features extracted from MTCNN and FaceNet.

Model Processing
Model | #Param | MAC | Embedding Dim. | Distance Metric | MAX78000 | MAX78002 |
---|---|---|---|---|---|---|
FaceNet | 27.91M | 962.9M | 512 | L2 | No | No |
Sequential | 340.67k | 59.07M | 64 | Cos | Yes | Yes |
MobileFaceNet | 853.06k | 371.3M | 64 | Cos | No | Yes |
- Reference:Face Identification Using MAX78000
References
[1]Zhang, Kaipeng et al. "Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks." IEEE Signal Processing Letters 23.10 (2016): 1499–1503.
[2]Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "FaceNet: A Unified Embedding for Face Recognition and Clustering." 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015)
Example 3: Abnormal Sound Detection System
Dual-core, low-power microcontroller
- Input device: microphone
- Inference Result: Abnormal sound detection
- System Features
Performs audio capture and inference every second.
Provides an edge AI-based small-scale development solution
Examples of abnormal sound detection
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Gunshot / Glass breaking / Siren
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Fall detection
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Mosquito detection
Use Case 4: Anomaly Detection System
- Input Device: Accelerometer
- Inference Result: Anomaly detection
- System Features
Realizes an AI model capable of detecting anomalies without requiring error data
Enables edge AI-based small-scale development solutions
Makes rapid system development and evaluation possible by using Analog Devices' accelerometer and signal chain.
Examples of abnormal sound detection
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Anomaly monitoring system for industrial motors.
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