6. Supported Devices

Tiny ML Tensorlab supports over 20 Texas Instruments microcontrollers across multiple device families. This section helps you understand device capabilities and choose the right MCU for your application.

Contents

• 6.1. Device Overview
  • 6.1.1. Supported Device Families
  • 6.1.2. Complete Device List
  • 6.1.3. Target Device Configuration
  • 6.1.4. NPU vs Non-NPU Devices
  • 6.1.5. Choosing a Device
• 6.2. NPU Guidelines
  • 6.2.1. NPU-Enabled Devices
  • 6.2.2. Layer Constraints
  • 6.2.3. Using NPU-Compatible Models
  • 6.2.4. Channel Multiples of 4
  • 6.2.5. Kernel Size Restrictions
  • 6.2.6. Compilation Preset
  • 6.2.7. Custom NPU-Compatible Models
  • 6.2.8. Troubleshooting NPU Compilation
  • 6.2.9. Performance Comparison
• 6.3. C2000 Family
  • 6.3.1. Overview
  • 6.3.2. Supported Devices
  • 6.3.3. F28P55 (Recommended)
  • 6.3.4. F28P65
  • 6.3.5. F2837
  • 6.3.6. C29x Family (F29H85, F29P58, F29P32)
  • 6.3.7. Typical Applications
  • 6.3.8. Development Tools
  • 6.3.9. Memory Considerations
  • 6.3.10. Next Steps
• 6.4. MSPM0 Family
  • 6.4.1. Overview
  • 6.4.2. Supported Devices
  • 6.4.3. MSPM0G5187 (NPU-Enabled)
  • 6.4.4. MSPM0G3507
  • 6.4.5. MSPM0G3519
  • 6.4.6. AM13 Family
  • 6.4.7. Power Considerations
  • 6.4.8. Memory Constraints
  • 6.4.9. Typical Applications
  • 6.4.10. Development Tools
  • 6.4.11. Getting Started
  • 6.4.12. Next Steps
• 6.5. Connectivity Devices
  • 6.5.1. Overview
  • 6.5.2. Supported Devices
  • 6.5.3. CC2755
  • 6.5.4. CC1352
  • 6.5.5. AM26x Family
  • 6.5.6. Typical Applications
  • 6.5.7. Power Optimization
  • 6.5.8. Memory Constraints
  • 6.5.9. Development Tools
  • 6.5.10. Wireless Protocol Considerations
  • 6.5.11. Getting Started
  • 6.5.12. Next Steps

6.6. Device Families at a Glance

Family	NPU Support	Architecture	Best For
C2000 DSP	F28P55 only	32/64-bit	Industrial control, motor drives, power systems
MSPM0	MSPM0G5187	Arm Cortex-M0+	Ultra-low power, cost-sensitive applications
MSPM33C	None	Arm Cortex-M33	Security-critical, high-performance edge
AM13	AM13E2	Arm Cortex-M33	High-performance edge with NPU acceleration
AM26x	None	Arm Cortex-R5	Industrial Ethernet, high-reliability systems
Connectivity	None	Arm Cortex-M33/M4	Wireless IoT, connected sensors

6.7. Complete Device List

C2000 Family:

Device	NPU	Description
F28P55	Yes	32-bit C28x, 150 MHz, NPU-accelerated (recommended for complex models)
F28P65	No	32-bit C28x, 150 MHz
F29H85	No	64-bit C29x core, high performance
F29P58	No	64-bit C29x core
F29P32	No	64-bit C29x core
F2837	No	Dual-core, 200 MHz
F28003	No	100 MHz, cost-effective
F28004	No	100 MHz, cost-effective
F280013	No	100 MHz, entry-level
F280015	No	120 MHz, entry-level

MSPM0 Family:

Device	NPU	Description
MSPM0G5187	Yes	80 MHz Cortex-M0+, NPU-accelerated, ultra-low power
MSPM0G3507	No	80 MHz Cortex-M0+, ultra-low power
MSPM0G3519	No	80 MHz Cortex-M0+, ultra-low power

MSPM33C Family:

Device	NPU	Description
MSPM33C32	No	160 MHz Cortex-M33, TrustZone security
MSPM33C34	No	160 MHz Cortex-M33, extended peripherals

AM13 Family:

Device	NPU	Description
AM13E2	Yes	160 MHz Cortex-M33, NPU-accelerated, TrustZone security

AM26x Family:

Device	NPU	Description
AM263	No	Quad-core Cortex-R5F, 400 MHz, industrial Ethernet
AM263P	No	Quad-core Cortex-R5F, 400 MHz, enhanced peripherals
AM261	No	Single-core Cortex-R5F, 400 MHz

Connectivity Devices:

Device	NPU	Description
CC2755	No	96 MHz Cortex-M33, Wi-Fi/Bluetooth
CC1352	No	Cortex-M4, multi-protocol wireless (Sub-GHz, BLE)

6.8. NPU vs Non-NPU Devices

NPU-Enabled Devices (F28P55, AM13E2, MSPM0G5187):

• Hardware-accelerated neural network inference

• Faster execution, lower power consumption

• Requires NPU-compatible model architectures

• Best for complex models with strict latency requirements

Non-NPU Devices:

• Software-based neural network execution

• More flexibility in model architecture

• Suitable for simpler models or less time-critical applications

• Lower cost options available

See NPU Guidelines for detailed information on designing NPU-compatible models.