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Ultra-Low-Power Arm Cortex-M4 Processor with FPU-Based Microcontroller with Convolutional Neural Network Accelerator

A New Breed of AI Micro Built to Enable Neural Networks to Execute at Ultra-Low Power

Product Details

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 microcontroller 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) accelerator 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 accelerator. The CNN engine has a weight storage memory of 442KB, and can support 1-, 2-, 4-, and 8-bit weights (supporting 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 execution 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 microcontroller core, with 512KB flash and up to 128KB SRAM. Multiple high-speed and low-power communications interfaces are supported, including I2S and a parallel camera interface (PCIF).

The device is available in 81-pin CTBGA (8mm x 8mm, 0.8mm pitch) and 130-pin WLP (4.6mm x 3.7mm, 0.35mm pitch) packages.

Key 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 Cache
    • Optional Error Correction Code (ECC-SEC-DED) for SRAM
    • 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 Optimaized for Deep Convolutional Neural Networks
    • 442k 8-bit Weight Capacity with 1,2,4,8-bit Weights
    • Programmable Input Image Size up to 1024 x 1024 pixels
    • 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 Battery Applications
    • 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 Power Consumption
    • 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 Generator

Applications/Uses

  • Audio Processing: Multi-Keyword Recognition, Sound Classification, Noise Cancellation
  • Facial Recognition
  • Object Detection and Classification
  • Time-Series Data Processing: Heart Rate/Health Signal Analysis, Multi-Sensor Analysis, Predictive Maintenance
Parametric specs for All Microcontrollers
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Technical Docs

Design & Development

Click any title below to view the detail page where available.

Description

The MAX78000 evaluation kit (EV kit) provides a platform for leveraging the capabilities of the MAX78000 to build new generations of artificial intelligence (AI) devices.

Onboard hardware includes a digital microphone, a gyroscope/ accelerometer, parallel camera module support and a 3.5in touch-enabled color TFT display. A secondary dis- play is driven by a power accumulator for tracking device power consumption over time. Uncommitted GPIO as well as analog inputs are readily accessible through 0.1in pin headers. Primary system power as well as UART access is provided by a USB Micro-B connector. A USB to SPI bridge provides rapid access to onboard memory, allow- ing large networks or images to load quickly.

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Features

  • Power Accumulator with Dedicated Display to Track Device Power over Time
  • Onboard Digital Microphone
  • Onboard Accelerometer/Gyroscope
  • SWD JTAG 10-Pin Header
  • RISC-V Coprocessor JTAG 10-Pin Header
  • 16M-Byte QSPI Flash
  • Select GPIOs Accessible through 0.1in Headers
  • Four ADC Inputs with Optional AA Filter
  • Touch-Enabled, 3.5in, 320 x 240 Color TFT Display
  • UART Access through USB Bridge
  • QSPI Memory Access through USB Bridge
  • All IC Power Rails May Be Isolated by Jumpers for Individual Current Measurements
  • Two General-Purpose LEDs and Two General- Purpose Pushbutton Switches

Description

The MAX78000FTHR is a rapid development platform to help engineers quickly implement ultra low-power, artificial intelligence (AI) solutions using the MAX78000 Arm® Cortex® M4F processor with an integrated Convolutional Neural Network accelerator. The board also includes the MAX20303 PMIC for battery and power management. The form factor is 0.9in x 2.6in dual-row header footprint that is compatible with Adafruit Feather Wing peripheral expansion boards. The board includes a variety of peripherals, such as a CMOS VGA image sensor, digital microphone, low-power stereo audio CODEC, 1MB QSPI SRAM, micro SD card connector, RGB indicator LED, and pushbutton. The MAX78000FTHR provides a power-optimized flexible platform for quick proof-of-concepts and early software development to enhance time to market.

Go to https://www.maximintegrated.com/en/products/MAX78000FTHR to get started developing with this board.

View Details

Features

  • MAX78000 Microcontroller
    • Dual Core: Arm Cortex-M4 Processor with FPU, 100MHz, RISC-V Coprocessor, 60MHz
    • 512KB Flash Memory
    • 128KB SRAM
    • 16KB Cache
    • Convolutional Neural Network Accelerator
    • 12-Bit Parallel Camera Interface
    • MAX20303 Wearable PMIC with Fuel Gauge
    • Charge from USB
    • On-Board DAPLink Debug and Programming
    • Interface for Arm Cortex-M4 processor with FPU
    • Breadboard Compatible Headers
    • Micro USB Connector
    • Micro SD Card Connector
  • Integrated Peripherals
    • RGB Indicator LED
    • User Pushbutton
    • CMOS VGA Image Sensor
    • Low-Power Stereo Audio CODEC
    • Digital Microphone
    • SWD Debugger
    • Virtual UART Console
    • 10-Pin Cortex Debug Header for RISC-V Coprocessor

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Support & Training

Search our knowledge base for answers to your technical questions.

Filtered Search

Our dedicated team of Applications Engineers are also available to answer your technical questions. Visit our support portal.

Parameters

Parametric specs for All Microcontrollers
MCU Core ARM Cortex-M4F
Internal Flash (KBytes) 1000
Core Clock Speed (MHz) (max) 100
Internal SRAM (KBytes) 128
Package/Pins CTBGA-CU/81
Budgetary
Price (See Notes)
$8.50 @1k

Key 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 Cache
    • Optional Error Correction Code (ECC-SEC-DED) for SRAM
    • 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 Optimaized for Deep Convolutional Neural Networks
    • 442k 8-bit Weight Capacity with 1,2,4,8-bit Weights
    • Programmable Input Image Size up to 1024 x 1024 pixels
    • 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 Battery Applications
    • 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 Power Consumption
    • 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 Generator

Applications/Uses

  • Audio Processing: Multi-Keyword Recognition, Sound Classification, Noise Cancellation
  • Facial Recognition
  • Object Detection and Classification
  • Time-Series Data Processing: Heart Rate/Health Signal Analysis, Multi-Sensor Analysis, Predictive Maintenance

Description

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 microcontroller 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) accelerator 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 accelerator. The CNN engine has a weight storage memory of 442KB, and can support 1-, 2-, 4-, and 8-bit weights (supporting 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 execution 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 microcontroller core, with 512KB flash and up to 128KB SRAM. Multiple high-speed and low-power communications interfaces are supported, including I2S and a parallel camera interface (PCIF).

The device is available in 81-pin CTBGA (8mm x 8mm, 0.8mm pitch) and 130-pin WLP (4.6mm x 3.7mm, 0.35mm pitch) packages.

Technical Docs

Support & Training

Search our knowledge base for answers to your technical questions.

Filtered Search

Our dedicated team of Applications Engineers are also available to answer your technical questions. Visit our support portal.