Model Attributes

operator_attributes:
  # Matches all operators
  - type: "*"
    attributes:
      weights_memory: tcm
      scratch_memory: tcm
  # Matches all operators of type CONV_2D
  - type: "CONV_2D"
    attributes:
      weights_memory: sram
      scratch_memory: sram
  # Match operators with IDs: 1, 2
  - ident:
        - 1
        - 2
    attributes:
      weights_memory: mram
      scratch_memory: mram

Attribute Precedence

For each operator in the model, the convert routine will select all attributes that match the operator type and ID. The attributes with higher specificity (i.e., more specific operator IDs) will take precedence over more general ones. The attributes will be consolidated into a single set of attributes for each operator. To verify the attributes applied to each operator, you can set the verbose level to 2 during conversion. This will provide detailed information about the attributes assigned to each operator in the generated module.

Example: Customizing Memory Placement

Let's say we have a model (simple.tflite) with the following architecture:

%%{ init: {
    "flowchart": {
      "nodeSpacing": 20,
      "rankSpacing": 30
    }
  }
}%%
flowchart TB;
    A[Input] ==> B["CONV_2D (ID:1)"]
    B ==> C["CONV_2D (ID:2)"]
    C ==> D["MAX_POOL_2D (ID:3)"]
    D ==> E["FULLY_CONNECTED (ID:4)"]
    E ==> F["SOFTMAX (ID:5)"]

Let's define an initial YAML configuration file (simple.yaml) for the conversion:

module_name: simple
module_type: zephyr
model_path: simple.tflite
output_path: simple.zip
prefix: simple
memory_planner: greedy
verbose: 1

By default, all operators are typically placed in TCM. However, TCM is extremely limited in size. In addition, CONV_2D, MAX_POOL_2D, and SOFTMAX are least sensitive to memory placement. Only FULLY_CONNECTED is sensitive to memory placement. Therefore, we can set the default behavior for these operators. We will by default map all operators to MRAM, and then attribute the memory placement for FULLY_CONNECTED to TCM. The new YAML configuration file (simple.yaml) will look like this:

module_name: simple
module_type: zephyr
model_path: simple.tflite
output_path: simple.zip
prefix: simple
memory_planner: greedy
verbose: 1
operator_attributes:
  # Default memory placement for all operators
  - type: *
    attributes:
      weights_memory: mram
      scratch_memory: sram
  # All Fully Connected operators will be placed in TCM
  - type: FULLY_CONNECTED
    attributes:
      weights_memory: tcm
      scratch_memory: tcm

After experimenting, let's say we find the CONV_2D operator w/ ID:1 is too slow when placed in MRAM. We can override the memory placement for this operator to SRAM. The final YAML configuration file (simple.yaml) will be the following:

module_name: simple
module_type: zephyr
model_path: simple.tflite
output_path: simple.zip
prefix: simple
memory_planner: greedy
verbose: 1
operator_attributes:
  # Default memory placement for all operators
  - type: *
    attributes:
      weights_memory: mram
      scratch_memory: sram
  # Place CONV_2D operator w/ ID:1 in SRAM
  - ident:
      - 1
    attributes:
      weights_memory: sram
      scratch_memory: sram
  # All Fully Connected operators will be placed in TCM
  - type: FULLY_CONNECTED
    attributes:
      weights_memory: tcm
      scratch_memory: tcm