MVP Compiler Configuration#

This page describes *.mlconf keys and merge order. For project setup, see Add Machine Learning to a New or Existing Project. For components, pipeline stages, and troubleshooting, see MVP Compiler.

Default Behavior#

A machine learning application compiles its model through an ml_model instance with a bound project *.mlconf. Set model: to the input .tflite path and regenerate the project. For more information, refer to Model path and project wiring and Add Machine Learning to a New or Existing Project.

With the default *.mlconf shipped for a new ml_model instance, the compiler applies memory planning, graph optimization, and memory optimization. The shipped component template enables graph and memory optimization on top of compiler built-in defaults. Custom memory regions and other advanced options are configurable form *.mlconf file in project.

Register compression always runs and cannot be turned off in *.mlconf. Memory planning uses extension defaults (see memory_planner). Studio supplies the target platform ID from your board or OPN during generation (Supported Platforms).

How Settings Combine#

When you regenerate a project, effective compiler settings are applied in layers. Each higher layer overrides the previous.

1. Compiler built-in defaults: Baseline values shipped with the compiler engine.

2. Component default template: inst.mlconf from the ml_model component merges on top of built-in defaults.

3. Your project *.mlconf: The file you edit under config/compiler/, bound to an ml_model instance in *.slcp (see Model path and project wiring).

4. Project generation: Simplicity Studio supplies the target OPN or board (resolved to a platform ID. For more information, refer to Supported Platforms) when the compiler runs. You normally do not set platform: in *.mlconf.

Nested sections in *.mlconf merge recursively. Unknown top-level keys are ignored. Regenerate the project after every change to a *.mlconf file so Studio applies the new settings.

Omit a key in your *.mlconf to keep the merged default from lower layers.

Supported Platforms#

SLC passes the target orderable part number (OPN) or board ID from your project configuration. The compiler sanitizes that value and converts it to a canonical platform ID used for the rest of the compile:

1. If the value already matches a known platform ID (case-insensitive), that ID is used.

2. Otherwise, the compiler infers a platform from the OPN or board pattern (for example, MG24-style parts → efr32xg24, brd4187 → efr32xg24, Si91x OPNs → siwg917).

3. If no rule matches, the sanitized value is passed through. If the driver does not recognize it, compilation fails.

You do not require to set platform: manually. The conversion happens automatically during generation. Unrecognized targets appear in the build log.

Compiler Platform IDs#

You can set accelerator: in *.mlconf to override the default for a platform. Typical accelerator IDs: auto (default), cmsis, mvpv1, sw_ref.

Configuration Format#

Project *.mlconf files use YAML with nested sections. The following is a quick overview of all settings with detailed descriptions in Configuration keys. Set model: and include only the sections you want to change.

model: my_model.tflite                     # input .tflite flatbuffer to compile
output: null                               # output path; SLC uses autogen
accelerator: auto                          # kernel backend for target platform
verbose: false                             # enable verbose compiler logging
log_level: INFO                            # compiler log level threshold
enable_debugging: false                    # enable compiler debug behavior
platform: auto                             # platform ID; SLC supplies target

disabled_layers: []                        # layer names excluded from compile
disabled_kernels: []                       # kernel names excluded from compile

graph_optimizer:
  enabled: true                            # enable graph optimization stage
  remove_constant_layers: true             # remove constant-only subgraphs

memory_optimizer:
  enabled: true                            # enable memory optimization stage
  weights_paging: single                   # weights paging mode
  buffer_reuse: false                      # reuse activation buffers when safe
  debugging_forced_buffer_sizes: null      # debug buffer size overrides

memory_planner:
  enabled: true                            # automatic tensor memory planning
  strategy: greedy                         # placement strategy for tensors
  plan_activations: false                  # include activations in planning
  plan_persistent_data: null               # plan persistent data placement

memory_specification:
  - type: PagingBuffer                     # memory region type name
    size: 32768                            # region size in bytes
    section: ".bss"                        # linker section for region
  - type: WeightsAndBiases2                # second weights and biases region
    size: 0                                # zero removes region from layout

codegen:
  enabled: true                            # enable firmware code generation
  shorten_paths: true                      # shorten paths in generated code
  output_format: source                    # header, source, or both
  include_paths: []                        # extra include paths for codegen
  linker_sections: {}                      # memory type to linker section map
  strip_strings: true                      # strip strings from model output
  generate_op_resolver: true               # emit model-specific op resolver
  prefix: model-stem                       # symbol prefix; uses .tflite stem
  logger_enabled: false                    # logger hooks in generated code
  model_header:
    filename: "{prefix}_generated.hpp"     # generated model header filename
    runtime_memory_size: null              # primary tensor arena size bytes
    model_memory_section: null             # linker section for model memory
    add_header_includes: true              # add standard includes to header
    generate_model_load_function: true     # emit model load helper function
    generate_op_resolver_instance: true    # emit op resolver in header
  model_parameters_header:
    enabled: false                         # emit parameters header file
    prefix: "SL_TFLITE_MODEL_{prefix}_"    # macro prefix for parameters
    filename: "{prefix}_generated_parameters.h"model: my_model.tflite                     # input .tflite flatbuffer to compile
output: null                               # output path; SLC uses autogen
accelerator: auto                          # kernel backend for target platform
verbose: false                             # enable verbose compiler logging
log_level: INFO                            # compiler log level threshold
enable_debugging: false                    # enable compiler debug behavior
platform: auto                             # platform ID; SLC supplies target

disabled_layers: []                        # layer names excluded from compile
disabled_kernels: []                       # kernel names excluded from compile

graph_optimizer:
  enabled: true                            # enable graph optimization stage
  remove_constant_layers: true             # remove constant-only subgraphs

memory_optimizer:
  enabled: true                            # enable memory optimization stage
  weights_paging: single                   # weights paging mode
  buffer_reuse: false                      # reuse activation buffers when safe
  debugging_forced_buffer_sizes: null      # debug buffer size overrides

memory_planner:
  enabled: true                            # automatic tensor memory planning
  strategy: greedy                         # placement strategy for tensors
  plan_activations: false                  # include activations in planning
  plan_persistent_data: null               # plan persistent data placement

memory_specification:
  - type: PagingBuffer                     # memory region type name
    size: 32768                            # region size in bytes
    section: ".bss"                        # linker section for region
  - type: WeightsAndBiases2                # second weights and biases region
    size: 0                                # zero removes region from layout

codegen:
  enabled: true                            # enable firmware code generation
  shorten_paths: true                      # shorten paths in generated code
  output_format: source                    # header, source, or both
  include_paths: []                        # extra include paths for codegen
  linker_sections: {}                      # memory type to linker section map
  strip_strings: true                      # strip strings from model output
  generate_op_resolver: true               # emit model-specific op resolver
  prefix: model-stem                       # symbol prefix; uses .tflite stem
  logger_enabled: false                    # logger hooks in generated code
  model_header:
    filename: "{prefix}_generated.hpp"     # generated model header filename
    runtime_memory_size: null              # primary tensor arena size bytes
    model_memory_section: null             # linker section for model memory
    add_header_includes: true              # add standard includes to header
    generate_model_load_function: true     # emit model load helper function
    generate_op_resolver_instance: true    # emit op resolver in header
  model_parameters_header:
    enabled: false                         # emit parameters header file
    prefix: "SL_TFLITE_MODEL_{prefix}_"    # macro prefix for parameters
    filename: "{prefix}_generated_parameters.h"

When codegen.prefix is model-stem (the default), it is replaced by the .tflite basename and sanitized for C identifiers. {prefix} in generated filenames and macro templates expands accordingly (for example, SL_TFLITE_MODEL_{prefix}_ in model_parameters_header).

The template above describes every supported key. The ml_model component ships inst.mlconf with model: null until you set the .tflite path in your project *.mlconf—see Default behavior.

Configuration Keys#

This section describes all supported configuration keys. Keys with nested sections are annotated with angled brackets where helpful (for example, codegen.model_header).

Many enabled fields accept true, false, or "auto" (the stage runs only when the selected accelerator supports it).

model#

• Type: string or null

• Default: null in the component template; required in project configuration files

• Description: Path to the input .tflite flatbuffer. Set a full host path to compile a flatbuffer outside the project, or register the file in *.slcp and use the filename (for example, my_model.tflite). See Model path and project wiring.

output#

• Type: string or null

• Default: null

• Description: Compiler output directory. No need to pass from .mlconf, SLC uses the autogen path.

platform#

• Type: string

• Default: auto

• Description: Compiler platform ID (see Supported Platforms). Normally supplied by SLC from the target OPN or board.

accelerator#

• Type: string

• Default: auto

• Description: Kernel backend for the target. Typical values: auto, cmsis, mvpv1, sw_ref, or other IDs supported for the platform.

verbose#

• Type: boolean

• Default: false

• Description: Enables verbose compiler logging.

log_level#

• Type: string or integer

• Default: INFO

• Description: DEBUG, INFO, WARNING, ERROR, CRITICAL, or a numeric logging level.

enable_debugging#

• Type: boolean

• Default: false

• Description: Enables compiler debug behavior.

disabled_layers#

• Type: list of strings

• Default: []

• Description: Layer names to exclude from compilation.

disabled_kernels#

• Type: list of strings

• Default: []

• Description: Kernel names to exclude from compilation.

graph_optimizer#

• Type: mapping

• Default: see Configuration format

memory_planner#

• Type: mapping

• Default: see Configuration format

Silicon Labs recommends leaving memory_planner.enabled at true for production firmware. Setting it to false disables automatic tensor placement. You must then provide a valid codegen.model_header.runtime_memory_size for your model and target. To change region sizes or linker sections without disabling the planner, use the memory_specification section.

memory_optimizer#

• Type: mapping

• Default: see Configuration format

memory_specification#

memory_specification is a top-level section in *.mlconf, separate from memory_planner. Memory planning consumes these region definitions when building the allocation plan.

• Type: list or null

• Default: platform-dependent regions Configuration format

• Description: List of memory region overrides merged with platform defaults. Use size: 0 to remove a region.

Each list entry is a mapping with the following keys:

Example:

memory_specification:
  - type: PagingBuffer
    size: 32768
    section: ".bss"
  - type: WeightsAndBiases2
    size: 0memory_specification:
  - type: PagingBuffer
    size: 32768
    section: ".bss"
  - type: WeightsAndBiases2
    size: 0

codegen#

• Type: mapping

• Default: see Configuration format

codegen.model_header#

codegen.model_parameters_header#

Model Path and Project Wiring#

The model key identifies which .tflite flatbuffer an ml_model instance compiles. You can point at a flatbuffer outside the project or include it in the project source tree.

Full host path — Set model: to an absolute path (for example, C:/models/my_model.tflite). The flatbuffer does not need to be listed in *.slcp.

Filename in tflite/ — To make the flatbuffer part of the project, register it in config_file: with directory: tflite, then set model: to the filename (for example, my_model.tflite). During generation, SLC stages the flatbuffer beside the *.mlconf under the virtual tflite/ directory.

Relative path — Resolved relative to the directory that contains the *.mlconf in the generated project tree (for example, ../tflite/my_model.tflite).

Wiring ml_model in *.slcp#

The following registers one model. For multiple models, see Multi-Model Support.

Register and override the component's config slots:

# my_project.slcp (excerpt)
component:
  - id: ml_model
    instance:
      - my_model
config_file:
  - path: config/compiler/my_model.mlconf
    directory: tflite
    override:
      component: ml_model
      file_id: ml_compiler_config
      instance: my_model
  - path: config/tflite/my_model.tflite
    directory: tflite# my_project.slcp (excerpt)
component:
  - id: ml_model
    instance:
      - my_model
config_file:
  - path: config/compiler/my_model.mlconf
    directory: tflite
    override:
      component: ml_model
      file_id: ml_compiler_config
      instance: my_model
  - path: config/tflite/my_model.tflite
    directory: tflite

The directory: tflite entries expose both files under the virtual tflite/ tree used during generation. The override: block binds the project *.mlconf to the ml_model instance's ml_compiler_config slot. The .tflite entry is optional when model: uses a full host path; register it in config_file: with directory: tflite when you want the flatbuffer in the project (no override required for the flatbuffer unless your template specifies one).

Align the instance name and *.mlconf basename for clarity (for example, instance my_model → my_model.mlconf). Compiled output filenames use the .tflite filename stem from model:, regardless of the instance name.

If you change which flatbuffer a configuration file applies to, update model: and the matching config_file: .tflite registration, then regenerate the project. Output filenames change with the new flatbuffer stem.

Minimal Project Configuration#

See Model path and project wiring for *.slcp registration. The shipped component template already enables graph and memory optimization.

model: my_model.tflite

graph_optimizer:
  enabled: true
memory_optimizer:
  enabled: truemodel: my_model.tflite

graph_optimizer:
  enabled: true
memory_optimizer:
  enabled: true

See Also#

• Add Machine Learning to a New or Existing Project for *.slcp and *.mlconf setup steps.

• MVP Compiler for integration overview and troubleshooting.