Simplicity Machine Learning Profiler#

Overview#

The Profiler is part of the Simplicity Machine Learning suite of tools. It helps you to understand how TensorFlow Lite for Microcontrollers (.tflite) models run on Silicon Labs embedded devices and optimize them before deployment.

The tool enables users to:

  • Correlate layer execution with clock rate, and memory pressure.

  • Build intuition for optimizing models to reduce stalls and improve inference latency

  • Identify performance bottlenecks during inference

  • Understand trade-offs between execution on the ARM Cortex-M CPU and the Matrix Vector Processor (MVP).

  • View and analyze your model layer by layer.

  • Convert PyTorch (.pt, .pth) and ONNX (.onnx) to .tflite models. The process is detailed in the Silicon Labs sml Converter page.

The tool is available as:

  • A graphical interface - Simplicity Machine Learning (GUI) or sml

  • A command-line interface - Simplicity Machine Learning (CLI) or sml-cli

It can also be launched directly from Simplicity Studio v6.

Who This Tool Is For#

This documentation is written primarily for embedded and ML engineers.
Product managers, data scientists, and sales engineers are expected to be sufficiently familiar with machine learning concepts to interpret the results.

The profiler focuses exclusively on execution performance, not model accuracy or output quality.

Key Concepts and Terminology#

Term

Meaning

Inference

One complete execution of a model

Layer

A neural network layer

Operator

A logical operation within a layer

Kernel

The concrete implementation that executes an operator

CPU

ARM Cortex-M core

MVP

Matrix Vector Processor hardware accelerator

Stall / Wait

Time spent idle due to memory or resource contention

Tensor Arena

Memory allocated for TFLM state

Quantization

Optimization technique to reduce model size

Perfetto

The trace visualization tool used

Running a Profiling Session from the GUI#

  1. Launch the Simplicity Machine Learning tool from Simplicity Studio v6

    1. Open Simplicity Studio and navigate to the Tools tab on the left panel.

    2. Either click on the Simplicity Machine Learning(GUI) button to check the overview and launch the tool or point to Simplicity Machine Learning(GUI) and click the play icon.

Simplicity Machine Learning launchSimplicity Machine Learning launch

  1. Or, Launch the Simplicity Machine Learning from the command line:

    slt launch sml

  2. Step 1 or 2 should launch the Simplicity Machine Learning. It is recommended to keep the window of Simplicity Machine Learning maximized or in full-screen for the best user experience.

Simplicity Machine Learning GUI Landing PageSimplicity Machine Learning GUI Landing Page

  1. Connect the board you on which you want to profile your model. The board will be detected automatically.

  2. Click the Browse button, navigate to the folder that contains the model file, and then select the .tflite model to profile.

  3. Optional. If your model is not available in the right format, you can convert PyTorch (.pt, .pth) and ONNX (.onnx) to .tflite models. The process is detailed in the Silicon Labs sml Converter page.

  4. Optional. After you select the model, click View Model to view and analyze the model in detail.

    Model Graph viewModel Graph view

  5. Optional. Select the kernel implementation that you want to run. By default, the HW Accelerated kernels are selected.

  6. Click the Profile Button.

    Profiling Session Setup and RunProfiling Session Setup and Run

    NOTE: See Troubleshooting section for handling any errors.

Outputs#

  • Summary tab includes:

    • Flash and RAM usage, CPU usage metrics, basic identification of the device

    • CPU and MVP accelerated cycle count, stalls, layer definitions

    • a way to export the data in either .json or .txt format.

    Profiling Summary Tab 1Profiling Summary Tab 1 Profiling Summary Tab 2Profiling Summary Tab 2

  • Perfetto trace tab: time-based execution and resource traces

    Profiling Perfetto Trace TabProfiling Perfetto Trace Tab

    NOTE: The profiler currently tracks only the ARM Cortex‑M CPU processor timeline. Usage and cycle information for the Matrix Vector Processor (MVP) is instead provided in the summary tab.

Running a Profiling Session from the CLI#

  1. Connect the board on which you want to profile your model. The board will be detected automatically, once connected.

  2. Find the "device ID" of the connected board. This is optional if only one device is connected. The SDM will detect the connected device.

    1. Linux/macOS

      $ ~/.silabs/slt/installs/archive/sdm-darwin-arm64/sdm adapter list
👉 Total adapter count: 1
↳ xxxxx [ usb wstk 440339411 xxxxx 127.0.0.1 ]

    2. Windows

    PS> $HOME\.silabs\slt\installs\archive\sdm-windows-amd64\sdm.exe adapter list
👉 Total adapter count: 1
  ↳ xxxxx [ usb wstk 440339411 xxxxx 127.0.0.1 ]

    The device ID is "440339411".

  3. Run Profiling

    sml profile /path/to/model_name.tflite 440339411

    NOTE: See Usage for more command line arguments. See Troubleshooting section for handling any errors.

Output#

The following is an example of the output you can expect to see on the command line terminal. Usernames and other sensitive information has been stubbed.

The log below includes:

  • Inference time

  • CPU vs MVP cycle breakdown

You can access:

  • text summary

  • detailed JSON report

🚀 Running profiling workflow...
   Device: 440333937 (BRD2601B)
   Model:  keyword_spotting_on_off_v2

📡 Step 1: Connecting to debug channel...

⚡ Step 2: Combining model with firmware and flashing......

📦 Step 3: Capturing packets and generating trace...

╔══════════════════════════════════════════════════════════╗
║           ML PROFILER - PROFILING SUMMARY                ║
╚══════════════════════════════════════════════════════════╝

SESSION SUMMARY
────────────────────────────────────────────────────────────
  Model Name                               keyword_spotting_on_off_v2
  Arena size                               88.0 KB
  Total Flash usage                        391.0 KB
  Total RAM usage                          96.0 KB
  Board                                    BRD2601B
  Order-able Part Number                   EFR32MG24B310F1536IM48
  Part Family                              xG24
  Flash                                    1,536 KB
  RAM                                      256 KB
  CPU                                      ARM Cortex-M33
  Accelerator                              MVP
  Total number of CPU cycles               284,142
  Number of Layers executed on CPU         2
  Layers executed on CPU                   RESHAPE, SOFTMAX
  CPU Utilization                          4.2 %
  Clock Rate                               78.0 MHz
  Total number of Accelerator cycles       6,491,814
  Total number of Accelerator stalls       2,000,816
  Total Accelerator MAC/cycle              0.55
  Number of Layers executed on Accelerator 11
  Layers executed on Accelerator           MAX_POOL_2D, FULLY_CONNECTED, CONV_2D
  Total inference time                     85.144 ms
  Inferences per second                    11.74
  Total number of operations               16,678,338
  Total number of MACs                     8,097,720

PER-LAYER SUMMARY
────────────────────────────────────────────────────────────────────────────────────────────────────────
                 |                  |                  |            |           Acc           |
     Layer       |   Input Shape    |   Output Shape   | CPU Cycles | ----------------------- | Time(ms)
                 |                  |                  |            | Cycles     | Stalls     |
-----------------+------------------+------------------+------------+------------+------------+---------
CONV_2D          | 1 x 99 x 68 x 1  | 1 x 99 x 68 x 10 | 29,860     | 965,680    | 228,087    | 12.763
MAX_POOL_2D      | 1 x 99 x 68 x 10 | 1 x 49 x 34 x 10 | 10,381     | 50,073     | 18         | 0.775
CONV_2D          | 1 x 49 x 34 x 10 | 1 x 49 x 34 x 20 | 31,113     | 2,299,263  | 740,554    | 29.877
MAX_POOL_2D      | 1 x 49 x 34 x 20 | 1 x 24 x 17 x 20 | 18,922     | 24,620     | 0          | 0.558
CONV_2D          | 1 x 24 x 17 x 20 | 1 x 24 x 17 x 40 | 31,701     | 2,117,241  | 691,564    | 27.551
MAX_POOL_2D      | 1 x 24 x 17 x 40 | 1 x 12 x 8 x 40  | 35,665     | 11,800     | 0          | 0.609
CONV_2D          | 1 x 12 x 8 x 40  | 1 x 12 x 8 x 40  | 32,258     | 917,766    | 305,283    | 12.18
MAX_POOL_2D      | 1 x 12 x 8 x 40  | 1 x 6 x 4 x 40   | 35,731     | 3,160      | 0          | 0.499
CONV_2D          | 1 x 6 x 4 x 40   | 1 x 6 x 4 x 20   | 32,867     | 101,265    | 34,916     | 1.72
MAX_POOL_2D      | 1 x 6 x 4 x 20   | 1 x 1 x 4 x 20   | 18,713     | 546        | 66         | 0.247
RESHAPE          | 1 x 1 x 4 x 20   | 1 x 80           | 1,188      | 0          | 66         | 0.015
FULLY_CONNECTED  | 1 x 80           | 1 x 3            | 2,514      | 400        | 131        | 0.037
SOFTMAX          | 1 x 3            | 1 x 3            | 3,229      | 0          | 131        | 0.041
────────────────────────────────────────────────────────────────────────────────────────────────────────

────────────────────────────────────────────────────────────
Generated: DATE/TIMER

📁 Profile data saved to:
   /$HOME/.silabs/sml/profiles/keyword_spotting_on_off_v2-2026-06-12T17-47-45Z

   Includes:
   • keyword_spotting_on_off_v2.pftrace (Perfetto trace)
   • captured-packets.json (decoded packets)
   • report.json (profiling data)
   • summary.txt (readable summary)

   📄 See summary.txt for the complete profiling summary.

✅ Profiling completed successfully!

📊 To view the trace, open the following file in https://ui.perfetto.dev/ , or re-run with --gui to open it automatically.
   Trace file: /$HOME/.silabs/sml/profiles/keyword_spotting_on_off_v2-2026-06-12T17-47-45Z/keyword_spotting_on_off_v2.pftrace

NOTE: If the --gui flag is provided the Perfetto trace will open in a window.

Usage#

sml --help
  Usage: sml [OPTIONS] COMMAND [ARGS]...

 Silicon Labs ML tooling.

╭─ Options ─────────────────────────────────────────────────────────────────────╮
│ --gui                      Open GUI after command completes (if supported).   │
│ --dry-run                  Validate and print the effective configuration,    │
│                            but do not execute the command.                    │
│ --log-level          TEXT  Logging verbosity. One of: error, warning, info,   │
│                            debug.                                             │
│                            [default: info]                                    │
│ --version    -v            Show version and exit.                             │
│ --help                     Show this message and exit.                        │
╰───────────────────────────────────────────────────────────────────────────────╯
╭─ Commands ────────────────────────────────────────────────────────────────────╮
│ profile  Profile a machine learning model on a Silicon Labs device. Emits a   │
│          Perfetto-compatible trace (.pftrace) or JSON summary (.json).        │
│ convert  Convert a PyTorch (.pt / .pth) or ONNX (.onnx) model to TFLite.      │
│ version  Show the version number.                                             │
╰───────────────────────────────────────────────────────────────────────────────╯
sml profile --help
 Usage: sml profile [OPTIONS] MODEL [DEVICE]

 Profile a machine learning model on a Silicon Labs device. Emits a
 Perfetto-compatible trace (.pftrace) or JSON summary (.json).

╭─ Arguments ──────────────────────────────────────────────────────────────────╮
│ *    model       TEXT      Path to model file (e.g. .tflite). Use '-' to     │
│                            read from stdin.                                  │
│                            [required]                                        │
│      device      [DEVICE]  Device identifier: Device ID, serial number,      │
│                            nickname, or IP address (on-device profiling). If │
│                            omitted, the only supported connected device is   │
│                            used.                                             │
╰──────────────────────────────────────────────────────────────────────────────╯
╭─ Options ────────────────────────────────────────────────────────────────────╮
│ --output  -o      TEXT  Output directory for all profiling results files. If │
│                         omitted, outputs to                                  │
│                         ~/.silabs/sml/profiles/<model>-<timestamp>/.         │
│ --kernel          TEXT  Kernel implementation to use. Accelerated kernels    │
│                         are chosen based on the value of device.             │
│                         [default: accelerated]                               │
│ --help                  Show this message and exit.                          │
╰──────────────────────────────────────────────────────────────────────────────╯

Understanding Performance#

The profiler presents a hierarchical execution view:

Inference → Layer → Operator → Kernel

Time-aligned tracks allow correlation between:

  • CPU vs MVP execution

  • memory usage

  • clock rate

Idle time per kernel helps identify when:

  • accelerator overhead dominates

  • memory stalls occur

  • CPU execution may be more efficient

Limitations#

  • Requires real Silicon Labs hardware, currently only supports xG24, xG26, and xG28 devices, specifically BRD2601B, BRD2608A, and BRD2705A dev kits and BRD2505A, BRD2506A, BRD4186C and BRD4187C boards.

  • Simulator support is in development

  • Does not auto-compare CPU vs MVP

  • Does not measure model accuracy. This is not a target of this tool. It is geared exclusively towards execution performance analysis.

Summary#

The Simplicity Machine Learning Profiler helps you analyze embedded ML performance by making execution behavior visible, comparable, and intuitive.

Troubleshooting#

"SDM Service is not available" warning#

SDM Service is not available warningSDM Service is not available warning

Solution#

  1. Verify if Simplicity Device Manager (SDM) is installed using, slt locate sdm.

  2. If you see no output on the console, install SDM using:

    1. slt install sdm through the CLI, or

    2. Simplicity Installer by following the steps mentioned in the Install using Simplicity Installer section. Search for "Simplicity Device Manager" instead of "Simplicity Machine Learning".

  3. Start SDM server.

    1. Linux/macOS

      ~/.silabs/slt/installs/archive/sdm-darwin-arm64/sdm server start

    2. Windows

    PS> $HOME\.silabs\slt\installs\archive\sdm-windows-amd64\sdm.exe server start

"No devices connected" message in the "Select Device" field#

No Device ConnectedNo Device Connected

Solution#

Connect the desired board on which you want to profile your models.

Any type of "Firmware preparation/flashing failed: Failed to combine model with firmware" error#

Examples of this type of error:

  1. Firmware preparation/flashing failed: Failed to combine model with firmware: 404 Not Found: Not Found

  2. Firmware preparation/flashing failed: Failed to combine model with firmware: Combine binary job failed: Error: Could not find function simpleCombineConvertBinaries. It is either typed wrong, you miss an adapter pack, or you need to upgrade one.

Solution#

  1. This issue is most commonly caused due to an older version of either Simplicity Device Manager or Simplicity Commander.

  2. Update Simplicity Commander to v1.22+. Install using slt install commander. Verify using slt locate commander.

  3. Update Simplicity Device Manager to v0.101.4+. Install using slt install sdm. Verify using slt locate sdm.

No Profiling Output#

GUI

no-profiling-summary-outputno-profiling-summary-output no-profiling-trace-outputno-profiling-trace-output

CLI

📦 Step 3: Capturing packets and generating trace...
   Captured 43 packets, building trace...
   Decoded 0 packets, generated 0 trace events

╔══════════════════════════════════════════════════════════╗
║           Simplicity Machine Learning - PROFILING SUMMARY                ║
╚══════════════════════════════════════════════════════════╝

────────────────────────────────────────────────────────────

Solution#

  1. Verify Simplicity Device Manager Service is up: sdm server status. If not, invoke sdm server start.

  2. Find the device ID, see Running a Profiling Session From the CLI section.

  3. Jump into admin console of your device: sdm terminal -a <device_id> -c admin.

  4. Verify the debug message version: dch message version, If the output is Message protocol version : 3, use Step 5 below.

  5. Invoke dch message version 2. The output must show Current version = 2.

  6. Execute a Profiling session again.