Arm CMSIS-NN: Neural Network Kernels for Cortex-M MCU

Introduction

With the growing demand for AI and machine learning (ML) at the edge, optimizing deep learning models for low-power microcontrollers has become a priority. Arm CMSIS-NN (Cortex Microcontroller Software Interface Standard – Neural Network) is an open-source collection of efficient neural network kernels designed specifically for Arm Cortex-M processors. It enables low-power embedded AI by providing optimized implementations of key deep learning operations, improving both performance and energy efficiency.

This article explores the architecture, benefits, and implementation of CMSIS-NN, highlighting its significance for edge AI applications.

What is CMSIS-NN?

CMSIS-NN is a set of optimized low-level neural network functions for Arm Cortex-M microcontrollers. It provides acceleration for common ML tasks without requiring high-end processors or GPUs.

Key Features of CMSIS-NN

• Optimized for Cortex-M MCUs: Designed specifically for Arm Cortex-M processors, minimizing memory usage and power consumption.
• Efficient Quantized Inference: Supports 8-bit integer (INT8) quantized models, reducing computational load.
• Reduced Latency & Memory Footprint: Uses highly optimized assembly and C implementations.
• Seamless TensorFlow Lite Micro Integration: CMSIS-NN can be used to accelerate TensorFlow Lite for Microcontrollers models.
• Support for Various Neural Network Operations: Includes optimized layers for convolutions, activation functions, pooling, fully connected layers, and more.

CMSIS-NN Architecture

CMSIS-NN provides a highly efficient set of operations designed to optimize ML inference on microcontrollers. The architecture consists of several components:

1. Optimized Neural Network Kernels

CMSIS-NN includes optimized implementations of:

• Convolutional Layers: Depthwise and standard 2D convolutions.
• Activation Functions: ReLU, sigmoid, and tanh.
• Pooling Layers: Max pooling and average pooling.
• Fully Connected Layers: Optimized matrix multiplications for deep learning inference.

2. Quantization Support

CMSIS-NN is designed for quantized 8-bit neural networks, which significantly reduces the computational burden compared to floating-point operations. Quantization improves inference speed and reduces memory usage, making ML viable on resource-constrained devices.

3. ARMv7-M and ARMv8-M Optimizations

• CMSIS-NN takes advantage of SIMD (Single Instruction Multiple Data) capabilities available in Cortex-M4, Cortex-M7, Cortex-M33, and Cortex-M55 processors.
• Uses CMSIS-DSP (Digital Signal Processing) functions to further optimize matrix operations and convolution calculations.

Setting Up CMSIS-NN in Embedded Projects

1. Installation and Requirements

To use CMSIS-NN in an embedded project, you need:

• A Cortex-M based microcontroller (e.g., STM32, NXP, NRF52, or other Arm-based MCUs)
• ARM Keil MDK or GCC toolchain for compiling the project
• CMSIS-NN library

Installation Steps

1. Clone the CMSIS-NN repository: git clone https://github.com/ARM-software/CMSIS_5.git cd CMSIS_5/CMSIS/NN
2. Include CMSIS-NN in your embedded project by adding the necessary header files: #include "arm_nnfunctions.h"
3. Compile the project using GCC or ARM Keil and flash it onto the target microcontroller.

Deploying an AI Model with CMSIS-NN

1. Train & Quantize a Model in TensorFlow

Develop a simple CNN model using TensorFlow, then convert it to an 8-bit quantized TensorFlow Lite model.

import tensorflow as tf

# Define a simple CNN model
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(16, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

2. Convert Model to TensorFlow Lite Format

converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()

3. Integrate the Model with CMSIS-NN

• Convert the .tflite file to a C array using:

xxd -i model.tflite > model_data.h

• In the embedded firmware, use CMSIS-NN functions to run inference:

#include "arm_nnfunctions.h"
#include "model_data.h"

int8_t input_data[28*28];
int8_t output_data[10];
const int32_t input_dim = 28*28;
const int32_t output_dim = 10;

arm_status status = arm_nn_convolve_s8(input_data, /* input */
                                       conv_weights, /* weights */
                                       conv_bias, /* bias */
                                       output_data /* output */);

Use Cases of CMSIS-NN

1. Wearable AI & Health Monitoring

• Heart rate anomaly detection
• Activity recognition in smartwatches

2. Industrial Predictive Maintenance

• Detecting faults in machinery vibration patterns
• Real-time anomaly detection using tiny ML models

3. Smart Home Automation

• Voice command recognition on microcontrollers
• Gesture-based control using embedded AI

4. IoT Sensor Data Processing

• Environmental monitoring (temperature, humidity, gas sensors)
• Wildfire detection using AI on remote sensors

CMSIS-NN vs Other Embedded ML Solutions

Conclusion

CMSIS-NN is an essential framework for bringing AI to ultra-low-power microcontrollers. With its optimized neural network kernels, quantization support, and seamless integration with TensorFlow Lite Micro, it enables developers to deploy highly efficient AI models on Cortex-M processors.

By leveraging CMSIS-NN, embedded developers can run ML applications faster, with lower power consumption, making it a perfect fit for IoT, smart wearables, and real-time AI inference at the edge.