6.4. MSPM0 Family

The MSPM0 family features Arm Cortex-M0+ processors optimized for ultra-low power and cost-sensitive applications.

6.4.1. Overview

MSPM0 devices are designed for:

  • Battery-powered IoT devices

  • Cost-sensitive consumer products

  • Always-on sensing applications

  • Simple predictive maintenance

The Cortex-M0+ core provides an excellent balance of performance and power efficiency for edge ML applications.

6.4.2. Supported Devices

Device

NPU

Frequency

Features

MSPM0G3507

No

80 MHz

Ultra-low power, cost-optimized

MSPM0G3519

No

80 MHz

Enhanced peripherals

MSPM0G5187

Yes

80 MHz

NPU-accelerated inference

6.4.3. MSPM0G5187 (NPU-Enabled)

The MSPM0G5187 is the recommended device for Tiny ML on MSPM0.

Key Features:

  • 80 MHz Arm Cortex-M0+ core

  • TINPU neural accelerator

  • 256 KB Flash

  • 64 KB SRAM

  • Ultra-low power modes

  • Integrated analog (12-bit ADC, DAC, comparators)

NPU Capabilities:

  • 8-bit quantized inference

  • Hardware convolution acceleration

  • Significantly faster than CPU-only inference

  • Optimized for small models (up to ~10k parameters)

Configuration:

common:
  target_device: 'MSPM0G5187'

training:
  model_name: 'CLS_1k_NPU'  # NPU-compatible models

compilation:
  preset_name: 'compress_npu_layer_data'

6.4.4. MSPM0G3507

Entry-level device without NPU, suitable for simpler models.

Key Features:

  • 80 MHz Cortex-M0+ core

  • 128 KB Flash

  • 32 KB SRAM

  • Low cost

  • Rich analog integration

Best For:

  • Simple classification tasks

  • Binary anomaly detection

  • Cost-constrained applications

Configuration:

common:
  target_device: 'MSPM0G3507'

training:
  model_name: 'CLS_100'  # Very small models

6.4.5. MSPM0G3519

Enhanced variant with additional peripherals.

Additional Features:

  • More GPIO pins

  • Additional communication interfaces

  • Extended temperature range options

Configuration:

common:
  target_device: 'MSPM0G3519'

6.4.6. AM13 Family

The AM13 family is a separate device family featuring Arm Cortex-M33 cores with NPU acceleration for high-performance edge ML applications.

Device

NPU

Frequency

Features

AM13E2

Yes

160 MHz

NPU-accelerated, TrustZone security

AM13E2 (NPU-Enabled):

The AM13E2 combines Cortex-M33 performance with NPU acceleration, making it ideal for security-critical and high-performance edge ML applications.

Key Features:

  • 160 MHz Arm Cortex-M33 core

  • TINPU neural accelerator

  • TrustZone security

  • Higher performance than MSPM0 family

common:
  target_device: 'AM13E2'

training:
  model_name: 'CLS_4k_NPU'

compilation:
  preset_name: 'compress_npu_layer_data'

6.4.7. Power Considerations

MSPM0 devices excel in low-power applications:

Active Power:

  • ~100 µA/MHz typical

  • Ideal for continuous sensing

Sleep Modes:

  • Standby: ~1-5 µA

  • Shutdown: <100 nA

Design Tips:

  1. Use smallest model that meets accuracy needs

  2. Batch inferences where possible

  3. Use wake-on-event for sparse data

  4. Consider inference duty cycling

6.4.8. Memory Constraints

MSPM0 devices have limited memory compared to C2000:

Device

Flash

SRAM

Recommended Model

MSPM0G3507

128 KB

32 KB

CLS_100 to CLS_500

MSPM0G3519

128 KB

32 KB

CLS_100 to CLS_500

MSPM0G5187

256 KB

64 KB

CLS_1k_NPU to CLS_4k_NPU

Memory Optimization:

# Use smaller feature extraction
data_processing_feature_extraction:
  feature_extraction_name: 'Generic_256Input_FFTBIN_32Feature_4Frame'

# Use quantized models
training:
  model_name: 'CLS_500_NPU'
  quantization: 1
  quantization_method: 'QAT'
  quantization_weight_bitwidth: 8
  quantization_activation_bitwidth: 8

6.4.9. Typical Applications

Wearable Devices:

  • Activity classification

  • Gesture recognition

  • Health monitoring

common:
  task_type: 'generic_timeseries_classification'
  target_device: 'MSPM0G5187'

training:
  model_name: 'CLS_1k_NPU'

Smart Sensors:

  • Vibration anomaly detection

  • Environmental monitoring

  • Acoustic event detection

common:
  task_type: 'generic_timeseries_anomalydetection'
  target_device: 'MSPM0G5187'

training:
  model_name: 'AD_1k_NPU'

Consumer Electronics:

  • Voice activity detection

  • Simple keyword spotting

  • Touch gesture classification

6.4.10. Development Tools

Code Composer Studio (CCS)

  • Install MSPM0 device support

  • Use SysConfig for pin configuration

  • Built-in debug support

MSPM0 SDK

  • Peripheral drivers

  • Example projects

  • Power management libraries

LaunchPad Development Kits

  • LP-MSPM0G3507: Entry-level evaluation

  • LP-MSPM0G5187: NPU evaluation (when available)

6.4.11. Getting Started

  1. Install CCS with MSPM0 support

  2. Install MSPM0 SDK

  3. Choose appropriate model size for your device

  4. Train with NPU-compatible model (for MSPM0G5187)

  5. Deploy using CCS

# Example configuration for MSPM0G5187
common:
  task_type: 'generic_timeseries_classification'
  target_device: 'MSPM0G5187'

dataset:
  dataset_name: 'generic_timeseries_classification'

training:
  model_name: 'CLS_1k_NPU'
  training_epochs: 20

compilation:
  enable: True

6.4.12. Next Steps