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ModusToolbox Machine Learning | Cypress Semiconductor

ModusToolbox Machine Learning

ModusToolbox™ Machine Learning (ML) enables you to rapidly evaluate and deploy Machine Learning models on Infineon MCUs. ModusToolbox ML is designed to work seamlessly with the ModusToolbox ecosystem and can be dropped into existing projects to enable machine learning tasks on low-power edge devices.

Easy Integration and Flexible Tools

ModusToolbox ML tools enables you to:

  • Import models from popular training frameworks such as TensorFlow™
  • Optimize the model for embedded platforms to reduce the size and complexity
  • Validate the performance of the optimized model by checking performance against test data
  • Generate optimized model code and libraries which are integrated with the ModusToolbox flow

ModusToolbox ML Software Tools

Optimized for Embedded Performance

ModusToolbox ML includes an embedded inference engine which supports optimized implementations of most popular Neural Network operators such as 1D/2D convolutions, a variety of activation functions as well as support for more complex operators for RNN networks such as GRU.

ModusToolbox ML adds configurators, tools, code examples and supporting libraries to help quickly get started with Machine Learning Model deployment on Infineon MCU’s.

ModusToolbox ML Software Tools

Download:

Please download the latest version of ModusToolbox and related patches to get access to ModusToolbox ML tooling.

Hardware Platforms Supported:

Features:

  • Import from Keras(H5) type file models
  • Validation GUI for various quantization levels
  • Supported operators and kernels
Network Type Operators/Primitives Activation Format Weights Format
MLP Dense, Batch Normalization Float, 16-bit, 8-bit Float, 16-bit, 8-bit
Conv1D Conv1D, MaxPool1D, MaxPooling1D, Batch Normalization Float, 16-bit, 8-bit Float, 16-bit, 8-bit
Conv2D Conv2D, MaxPooling2D, SeparableConv2D, DepthwiseConv2D, Average Pooling 1D, Global Max Pooling 1D Float, 16-bit, 8-bit Float, 16-bit, 8-bit
Recurrent GRU Float, 16-bit, 8-bit Float, 16-bit, 8-bit
Reshape Reshape 2D, Flatten, Input Layer Float, 16-bit, 8-bit Float, 16-bit, 8-bit
Merge Concatenate, Add Float, 16-bit, 8-bit Float, 16-bit, 8-bit
Activation Relu, tanh, Softmax, sigmoid Float, 16-bit, 8-bit Float, 16-bit, 8-bit
Auxiliary Dropout, Activation Float, 16-bit, 8-bit Float, 16-bit, 8-bit

Development Kits

PSoC® 62S2 Wi-Fi BT Pioneer Kit (CY8CKIT-062S2-43012)

CY8CKIT-062S2-43012

Development Kits

  • PSoC62 MCU with upto 2M Flash and 1M SRAM
  • CYW43012 WiFi + BT Combo Chip(Murata 1LV module)
  • Arduino compatible shield headers
PSoC® 64 Secure Boot Wi-Fi Bluetooth Pioneer Kit (CY8CKIT-064B0S2-4343W)

CY8CKIT-064B0S2-4343W

Development Kits

  • PSoC 64 MCU (CYB0644ABZI-S2D44) with 1856KB of Flash memory, 920KB of SRAM memory
  • Murata 1DX Wi-Fi Bluetooth module (802.11 a/b/g/n/ac)
  • On-board debugger / programmer (KitProg3)
CY8CKIT-028-SENSE Shield

CY8CKIT-028-SENSE Shield

Development Kits

  • XENSIV low noise microphones
  • XENSIV absolute barometric pressure sensors
  • 9-axis motion sensor
  • OLED display

To get started with ModusToolbox ML, please get the below hardware evaluation kits:

Please follow the steps in the ModusToolbox ML User Guide listed in the documentation which will guide you through the setup.

Document Type Title English Chinese Japanese
User Guide ModusToolbox ML User Guide    
ModusToolbox ML Configurator Guide    
Kit Guide PSoC® 62S2 Wi-Fi BT Pioneer Kit (CY8CKIT-062S2-43012)    
PSoC® 64 "Secure Boot” Wi-Fi BT Pioneer Kit (CY8CKIT-064B0S2-4343W)