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Artificial Intelligence Microcontroller with Ultra-
Low-Power Convolutional Neural Network
Artificial intelligence (AI) requires extreme computational
horsepower, but Maxim is cutting the power cord from
AI insights. The MAX78000 is a new breed of AI mi-
crocontroller built to enable neural networks to execute
at ultra-low power and live at the edge of the IoT. This
product combines the most energy-efficient AI processing
with Maxim's proven ultra-low power microcontrollers. Our
hardware-based convolutional neural network (CNN) ac-
celerator enables battery-powered applications to execute
AI inferences while spending only microjoules of energy.
The MAX78000 is an advanced system-on-chip featuring
an Arm® Cortex®-M4 with FPU CPU for efficient system
control with an ultra-low-power deep neural network accel-
erator. The CNN engine has a weight storage memory of
442KB, and can support 1-, 2-, 4-, and 8-bit weights (sup-
porting networks of up to 3.5 million weights). The CNN
weight memory is SRAM-based, so AI network updates
can be made on the fly. The CNN engine also has 512KB
of data memory. The CNN architecture is highly flexible,
allowing networks to be trained in conventional toolsets
like PyTorch® and TensorFlow®, then converted for exe-
cution on the MAX78000 using tools provided by Maxim.
In addition to the memory in the CNN engine, the
MAX78000 has large on-chip system memory for the mi-
crocontroller core, with 512KB flash and up to 128KB
SRAM. Multiple high-speed and low-power communica-
tions interfaces are supported, including I2S and a parallel
camera interface (PCIF).
The device is available in a 81-pin CTBGA (8mm x 8mm,
0.8mm pitch) package.
● Object Detection and Classification
● Audio Processing: Multi-Keyword Recognition, Sound
Classification, Noise Cancellation
● Facial Recognition
● Time-Series Data Processing: Heart Rate/Health
Signal Analysis, Multi-Sensor Analysis, Predictive
Benefits and Features
● Dual Core Ultra-Low-Power Microcontroller
• Arm Cortex-M4 Processor with FPU up to 100MHz
• 512KB Flash and 128KB SRAM
• Optimized Performance with 16KB Instruction
• Optional Error Correction Code (ECC-SEC-DED)
• 32-Bit RISC-V Coprocessor up to 60MHz
• Up to 52 General-Purpose I/O Pins
• 12-Bit Parallel Camera Interface
• One I2S Master/Slave for Digital Audio Interface
● Neural Network Accelerator
• Highly Optimized for Deep Convolutional Neural
• 442k 8-Bit Weight Capacity with 1,2,4,8-Bit Weights
• Programmable Input Image Size up to 1024 x 1024
• Programmable Network Depth up to 64 Layers
• Programmable per Layer Network Channel Widths
up to 1024 Channels
• 1 and 2 Dimensional Convolution Processing
• Streaming Mode
• Flexibility to Support Other Network Types,
Including MLP and Recurrent Neural Networks
● Power Management Maximizes Operating Time for
• Integrated Single-Inductor Multiple-Output (SIMO)
Switch-Mode Power Supply (SMPS)
• 2.0V to 3.6V SIMO Supply Voltage Range
• Dynamic Voltage Scaling Minimizes Active Core
• 22.2μA/MHz While Loop Execution at 3.0V from
Cache (CM4 Only)
• Selectable SRAM Retention in Low-Power Modes
with Real-Time Clock (RTC) Enabled
● Security and Integrity
• Available Secure Boot
• AES 128/192/256 Hardware Acceleration Engine
• True Random Number Generator (TRNG) Seed
Ordering Information appears at end of data sheet.
Arm and Cortex are registered trademarks of Arm Limited (or its subsidiaries) in the US and/or elsewhere.
CoreMark is a registered trademark of the Embedded Microprocessor Benchmark Consortium.
Motorola is a registered trademark of Motorola Trademark Holdings, LLC.
PyTorch is a trademark of Facebook, Inc.
TensorFlow is a trademark of Google, Inc.
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