MAX78000EXG+

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).

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

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 Optimized for Deep Convolutional Neural Networks
• 442k 8-Bit Weight Capacity with 1,2,4,8-Bit Weights
• Programmable Input Image Size up to 1024x1024 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) SeedGenerator