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Maxim Integrated

Maxim Integrated MAX78000 Arm Cortex-M4 Processor

Enabling neural networks to execute at ultra-low power

The Maxim Integrated MAX78000 is a breakthrough in AI microcontrollers, built to extend IoT capabilities to the edge with the most energy-efficient Artificial Intelligence processing.

The Maxim Integrated MAX78000 is an advanced system-on-chip featuring an Arm® Cortex®-M4 with FPU CPU for efficient system control with a convolutional neural network accelerator (CNN) that enables battery-powered applications to execute AI inferences while spending only microjoules of energy.

Convolutional Network Accelerator (CNN)

Maxim’s MAX78000 features a CNN engine that has a weight storage memory of just 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 MAX78000 CNN engine also has 512KB of data memory and its 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.

MAX78000 System Memory

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).

MAX78000 System Memory

The MAX78000 processor 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.

MAX78000 System Memory

  • 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 8bit 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
    • 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

  • 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 Maintenance