A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

nn-Meter is a novel and efficient system to accurately predict the inference latency of DNN models on diverse edge devices. The key idea is dividing a whole model inference into kernels, i.e., the execution units of fused operators on a device, and conduct kernel-level prediction. We currently evaluate four popular platforms on a large dataset of 26k models. It achieves 99.0% (mobile CPU), 99.1% (mobile Adreno 640 GPU), 99.0% (mobile Adreno 630 GPU), and 83.4% (Intel VPU) prediction accuracy.

The current supported hardware and inference frameworks:

Device Framework Processor +-10% Accuracy Hardware name
Pixel4 TFLite v2.1 CortexA76 CPU 99.0% cortexA76cpu_tflite21
Mi9 TFLite v2.1 Adreno 640 GPU 99.1% adreno640gpu_tflite21
Pixel3XL TFLite v2.1 Adreno 630 GPU 99.0% adreno630gpu_tflite21
Intel Movidius NCS2 OpenVINO2019R2 Myriad VPU 83.4% myriadvpu_openvino2019r2

nn-Meter has achieved the Mobisys 21 Best Paper Award! For more details, please check out paper:

nn-Meter: towards accurate latency prediction of deep-learning model inference on diverse edge devices

Who should consider using nn-Meter

  • Those who want to get the DNN inference latency on mobile and edge devices with no deployment efforts on real devices.
  • Those who want to run hardware-aware NAS with NNI.
  • Those who want to build latency predictors for their own devices.


Currently nn-Meter has been tested on Linux and Windows system. Windows 10, Ubuntu 16.04 and 20.04 with python 3.6.10 are tested and supported. Please first install python3 before nn-Meter installation.

We haven't released this package yet, so development installation is required. To install the latest version of nn-Meter, users should install the package through source code. First git clone nn-Meter package to local:

git clone [email protected]:microsoft/nn-Meter.git
cd nn-Meter

Then simply run the following pip install in an environment that has python >= 3.6. The command will complete the automatic installation of all necessary dependencies and nn-Meter.

pip install .

nn-Meter is a latency predictor of models with type of tensorflow, pytorch, onnx, nn-meter IR graph and NNI IR graph. To use nn-Meter for specific model type, you also need to install corresponding pacakges. The well tested versions are listed below:

Testing Model Tpye Requirments
Tensorflow tensorflow==1.15.0
Torch onnx==1.9.0, torch==1.9.0, torchvision==0.10.0
Onnx onnx==1.9.0
nn-Meter IR graph ---
NNI IR graph nni==2.4

Please also check the versions of numpy and scikit_learn. The different versions may change the prediction accuracy of kernel predictors.

The stable version of wheel binary pacakge will be released soon.


To apply for hardware latency prediction, nn-Meter provides two types of interfaces´╝Ü

  • command line nn-meter after nn-meter installation.
  • Python binding provided by the module nn_meter

Here is a summary of supported inputs of the two methods.

Testing Model Type Command Support Python Binding
Tensorflow Checkpoint file dumped by tf.saved_model() and endwith .pb Checkpoint file dumped by tf.saved_model and endwith .pb
Torch Models in torchvision.models Object of torch.nn.Module
Onnx Checkpoint file dumped by onnx.save() and endwith .onnx Checkpoint file dumped by onnx.save() or model loaded by onnx.load()
nn-Meter IR graph Json file in the format of nn-Meter IR Graph dict object following the format of nn-Meter IR Graph
NNI IR graph - NNI IR graph object

In both methods, users could appoint predictor name and version to target a specific hardware platform (device). Currently, nn-Meter supports prediction on the following four configs:

Predictor (device_inferenceframework) Processor Category Version
cortexA76cpu_tflite21 CPU 1.0
adreno640gpu_tflite21 GPU 1.0
adreno630gpu_tflite21 GPU 1.0
myriadvpu_openvino2019r2 VPU 1.0

Users can get all predefined predictors and versions by running

# to list all predefined predictors
nn-meter --list-predictors 

Predict latency of saved CNN model

After installation, a command named nn-meter is enabled. To predict the latency for a CNN model with a predefined predictor in command line, users can run the following commands

# for Tensorflow (*.pb) file
nn-meter --predictor <hardware> [--predictor-version <version>] --tensorflow <pb-file_or_folder> 

# for ONNX (*.onnx) file
nn-meter --predictor <hardware> [--predictor-version <version>] --onnx <onnx-file_or_folder>

# for torch model from torchvision model zoo (str)
nn-meter --predictor <hardware> [--predictor-version <version>] --torchvision <model-name> <model-name>... 

# for nn-Meter IR (*.json) file
nn-meter --predictor <hardware> [--predictor-version <version>] --nn-meter-ir <json-file_or_folder> 

--predictor-version <version> arguments is optional. When the predictor version is not specified by users, nn-meter will use the latest verison of the predictor.

nn-Meter can support batch mode prediction. To predict latency for multiple models in the same model type once, user should collect all models in one folder and state the folder after --[model-type] liked argument.

It should also be noted that for PyTorch model, nn-meter can only support existing models in torchvision model zoo. The string followed by --torchvision should be exactly one or more string indicating name(s) of some existing torchvision models.

Convert to nn-Meter IR Graph

Furthermore, users may be interested to convert tensorflow pb-file or onnx file to nn-Meter IR graph. Users could convert nn-Meter IR graph and save to .json file be running

# for Tensorflow (*.pb) file
nn-meter getir --tensorflow <pb-file> [--output <output-name>]

# for ONNX (*.onnx) file
nn-meter getir --onnx <onnx-file> [--output <output-name>]

Output name is default to be /path/to/input/file/<input_file_name>_<model-type>_ir.json if not specified by users.

Use nn-Meter in your python code

After installation, users can import nn-Meter in python code

from nn_meter import load_latency_predictor

predictor = load_latency_predictor(hardware_name, hardware_predictor_version) # case insensitive in backend

# build your model (e.g., model instance of torch.nn.Module)
model = ... 

lat = predictor.predict(model, model_type) # the resulting latency is in unit of ms

By calling load_latency_predictor, user selects the target hardware and loads the corresponding predictor. nn-Meter will try to find the right predictor file in ~/.nn_meter/data. If the predictor file doesn't exist, it will download from the Github release.

In predictor.predict, the allowed items of the parameter model_type include ["pb", "torch", "onnx", "nnmeter-ir", "nni-ir"], representing model types of tensorflow, torch, onnx, nn-meter IR graph and NNI IR graph, respectively.

Users could view the information all built-in predictors by list_latency_predictors or view the config file in nn_meter/configs/predictors.yaml.

Users could get a nn-Meter IR graph by applying model_file_to_graph and model_to_graph by calling the model name or model object and specify the model type. The supporting model types of model_file_to_graph include "onnx", "pb", "torch", "nnmeter-ir" and "nni-ir", while the supporting model types of model_to_graph include "onnx", "torch" and "nni-ir".

Hardware-aware NAS by nn-Meter and NNI

To empower affordable DNN on the edge and mobile devices, hardware-aware NAS searches both high accuracy and low latency models. In particular, the search algorithm only considers the models within the target latency constraints during the search process.

Currently we provides example of end-to-end multi-trial NAS, which is a random search algorithm on SPOS NAS search space. More examples of more hardware-aware NAS and model compression algorithms are coming soon.

To run multi-trail SPOS demo, NNI should be installed through source code by following NNI Doc

python setup.py develop

Then run multi-trail SPOS demo:

python ${NNI_ROOT}/examples/nas/oneshot/spos/multi_trial.py

How the demo works

Refer to NNI Doc for how to perform NAS by NNI.

To support hardware-aware NAS, you first need a Strategy that supports filtering the models by latency. We provide such a filter named LatencyFilter in NNI and initialize a Random strategy with the filter:

simple_strategy = strategy.Random(model_filter=LatencyFilter(threshold=100, predictor=base_predictor))

LatencyFilter will predict the models' latency by using nn-Meter and filter out the models whose latency with the given predictor are larger than the threshold (i.e., 100 in this example).
You can also build your own strategies and filters to support more flexible NAS such as sorting the models according to latency.

Then, pass this strategy to RetiariiExperiment:

exp = RetiariiExperiment(base_model, trainer, strategy=simple_strategy)

exp_config = RetiariiExeConfig('local')
exp_config.dummy_input = [1, 3, 32, 32]

exp.run(exp_config, port)

In exp_config, dummy_input is required for tracing shape info.