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Maxim Integrated MAX78000 Ultra-Low-Power Arm Cortex-M4 Processor

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Maxim Integrated MAX78000 Ultra-Low-Power Arm Cortex-M4 Processor is used for Artificial intelligence (AI) applications that require extreme computational horsepower. The MAX78000 is built to enable neural networks and combines energy-efficient AI processing with ultra-low-power microcontrollers. The hardware-based convolutional neural network (CNN) accelerator enables battery-powered applications to execute AI inferences while spending only microjoules of energy.

  • 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 Optimized 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 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

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