6.5. Connectivity Devices

TI’s connectivity devices combine wireless communication with edge ML capabilities for IoT applications.

6.5.1. Overview

Connectivity devices enable:

  • Wireless sensor networks with edge AI

  • Smart home and building automation

  • Industrial IoT with local inference

  • Bluetooth and Sub-GHz sensing applications

These devices perform ML inference locally, reducing latency and network bandwidth while maintaining wireless connectivity.

6.5.2. Supported Devices

SimpleLink CC27xx Family

Device

Core

Features

CC2755

Cortex-M33 (96 MHz)

Bluetooth 5.4 LE, Thread, Zigbee, Matter

SimpleLink CC13xx Family

Device

Core

Features

CC1352

Cortex-M4 (48 MHz)

Sub-GHz, Bluetooth 5.0 LE, multi-protocol

6.5.3. CC2755

The CC2755 is a modern wireless MCU with strong ML capabilities.

Key Features:

  • 96 MHz Arm Cortex-M33 core

  • Bluetooth 5.4 LE Audio

  • Thread and Zigbee support

  • Matter protocol ready

  • 1 MB Flash

  • 256 KB SRAM

  • TrustZone security

ML Capabilities:

  • CPU-based inference (no NPU)

  • Support for models up to ~10k parameters

  • Suitable for classification and anomaly detection

Configuration:

common:
  target_device: 'CC2755'

training:
  model_name: 'CLS_4k'  # Standard models

6.5.4. CC1352

Multi-band wireless MCU for Sub-GHz and Bluetooth applications.

Key Features:

  • 48 MHz Arm Cortex-M4F core

  • Sub-GHz radio (for long range)

  • Bluetooth 5.0 LE

  • 352 KB Flash

  • 80 KB SRAM

  • Ultra-low power

ML Capabilities:

  • More constrained than CC2755

  • Best for small models (<4k parameters)

  • Ideal for simple classification tasks

Configuration:

common:
  target_device: 'CC1352'

training:
  model_name: 'CLS_1k'  # Small models

6.5.5. AM26x Family

The AM26x family uses Arm Cortex-R5F cores for real-time industrial applications with Ethernet connectivity.

Device

Cores

Features

AM263

Quad R5F (400 MHz)

Industrial Ethernet, high performance

AM263P

Quad R5F (400 MHz)

Enhanced security features

AM261

Single R5F (400 MHz)

Cost-optimized industrial

Key Features:

  • Real-time capable Cortex-R5F cores

  • Industrial Ethernet (EtherCAT, PROFINET, EtherNet/IP)

  • High-speed ADCs and PWMs

  • Suitable for larger ML models

Configuration:

common:
  target_device: 'AM263'  # or AM263P, AM261

training:
  model_name: 'CLS_6k'  # Can handle larger models

6.5.6. Typical Applications

Wireless Sensor Networks

Deploy ML-enabled sensors with wireless backhaul:

# Vibration sensor with Bluetooth reporting
common:
  task_type: 'generic_timeseries_anomalydetection'
  target_device: 'CC2755'

training:
  model_name: 'AD_2k'

Use cases:

  • Structural health monitoring

  • Environmental sensing

  • Asset tracking with condition monitoring

Smart Home/Building

Local inference for privacy and responsiveness:

# Occupancy detection
common:
  task_type: 'generic_timeseries_classification'
  target_device: 'CC2755'

training:
  model_name: 'CLS_2k'

Use cases:

  • Occupancy sensing

  • HVAC optimization

  • Security systems

Industrial IoT

Edge inference with industrial protocols:

# Motor monitoring with Ethernet reporting
common:
  task_type: 'generic_timeseries_classification'
  target_device: 'AM263'

dataset:
  dataset_name: 'motor_fault_classification_dsk'

training:
  model_name: 'CLS_6k'

Use cases:

  • Predictive maintenance

  • Quality inspection

  • Process anomaly detection

Long-Range IoT (Sub-GHz)

Remote sensing with minimal power:

# Remote vibration sensor
common:
  task_type: 'generic_timeseries_anomalydetection'
  target_device: 'CC1352'

training:
  model_name: 'AD_500'  # Minimal model

6.5.7. Power Optimization

Connectivity devices often run on batteries:

Duty Cycling

Run inference periodically, sleep between:

  • Wake on timer or sensor threshold

  • Perform inference

  • Transmit only if anomaly detected

  • Return to sleep

Model Size vs Battery Life

Smaller models use less energy per inference:

Model Size

Inference Energy

Battery Impact

500 params

~10 µJ

Minimal

2k params

~50 µJ

Low

4k params

~150 µJ

Moderate

Transmission Optimization

  • Send only alerts, not raw data

  • Batch non-urgent communications

  • Use lowest sufficient TX power

6.5.8. Memory Constraints

Connectivity devices have varying memory:

Device

Flash

RAM

Recommended Model

CC2755

1 MB

256 KB

Up to CLS_6k

CC1352

352 KB

80 KB

Up to CLS_2k

AM263

2 MB

512 KB

Up to CLS_13k

Note: Wireless stack consumes significant memory. Plan model size accordingly.

6.5.9. Development Tools

Code Composer Studio (CCS)

  • Install SimpleLink SDK for CC27xx/CC13xx

  • Install AM26x SDK for AM26x devices

  • Use SysConfig for wireless stack configuration

SimpleLink SDK

  • Wireless protocol stacks

  • Example applications

  • Power management

TI 15.4-Stack

For Sub-GHz mesh networks with CC13xx.

Industrial Communications SDK

For AM26x industrial Ethernet protocols.

6.5.10. Wireless Protocol Considerations

Choose protocol based on application needs:

Protocol

Device

Best For

Bluetooth LE

CC2755, CC1352

Short range, smartphone integration

Thread/Zigbee

CC2755

Home automation mesh

Sub-GHz

CC1352

Long range, building penetration

Industrial Ethernet

AM26x

Factory automation, real-time

6.5.11. Getting Started

  1. Choose device based on connectivity needs

  2. Install appropriate SDK

  3. Account for wireless stack memory usage

  4. Select model size within remaining memory

  5. Test inference + communication together

# Example: BLE sensor node
common:
  task_type: 'generic_timeseries_anomalydetection'
  target_device: 'CC2755'

dataset:
  dataset_name: 'your_sensor_dataset'

training:
  model_name: 'AD_2k'
  training_epochs: 30

compilation:
  enable: True

6.5.12. Next Steps