Release notes

1.6.0

Main changes

ElcoreNN DSP library optimizations.
Support Tanh and InstanceNormalization ONNX operators.

ElcoreNN DSP

Increase performance of following layers: Concat, Reshape, Split.

1.5.0

Main changes

New ElcoreNN CPU API functions.
ElcoreNN DSP library optimizations.
Update onnx-elcorenn to v1.3.3.
Support new ONNX operators and Keras layers.
elcorenn-examples: Installation path has been changed.

ElcoreNN CPU API

InvokeModel:

Supported different types of model input data.

Added multiple outputs support for input dmabuf (for models with one input).

dmabuf_input_user_pointer_output.cpp

#include "elcorenn/elcorenn.h"

int main() {
  // Init ElcoreNN resources
  InitBackend();
  // Load model from files
  auto id = LoadModel("path-to-model-json-file", "path-to-weights-bin-file");

  // Read output layers shapes
  int outputs_number = GetOutputsNumber(id);
  std::vector<std::vector<int>> outputs_shapes;
  for (int i = 0; i < outputs_number; i++){
    uint32_t shape_arr[MAX_NDIM_VALUE + 1];
    GetOutputShape(id, i, shape_arr);
    std::vector<int> shape(shape_arr[0]);
    for (int j = 0; j < shape_arr[0]; j++) {
      shape[j] = shape_arr[j + 1];
    }
    outputs_shapes.push_back(shape);
  }

  std::vector<int> buffers;
  uint32_t batch_size;
  // Create and fill or use already existing dmabufs to push it back to
  // buffers vector. Number of dmabufs in buffers should be equal to
  // batch_size.
  // ...
  // batch_size is a batch dimension value in input_data, output_data

  // Allocate memory for outputs data
  std::vector<std::vector<float>> outputs_data;
  for (int i = 0; i < outputs_number; i++){
    auto shape = outputs_shapes[i];
    int size = 1;
    for (auto dim_size: shape){
      size = size * dim_size;
    }
    std::vector<float> output_data(size * batch_size);
    outputs_data.push_back(output_data);
  }

  // Prediction
  float* outputs[outputs_number];
  for (int j = 0; j < outputs_number; j++) {
    outputs[j] = outputs_data[j].data();
  }
  InvokeModel(id, buffers.data(), outputs, batch_size);
}

Added support for input dmabuf and output dmabuf (for models with one input and one output).

dmabuf_input_dmabuf_output.cpp

#include "elcorenn/elcorenn.h"

int main() {
  // Init ElcoreNN resources
  InitBackend();
  // Load model from files
  auto id = LoadModel("path-to-model-json-file", "path-to-weights-bin-file");

  std::vector<int> input_buffers;
  std::vector<int> output_buffers;
  uint32_t batch_size;
  // Create or use already existing dmabufs to push it back to buffers
  // vector. Number of dmabufs in buffers should be equal to batch_size.
  // ...
  // batch_size is a batch dimension value in input_data, output_data

  // Prediction
  InvokeModel(id, input_buffers.data(), output_buffers.data(), batch_size);
}

InitBackend: Add argument dsp_heap to set dsp heap size.
LoadModel: Add argument optimization to set data type of computation.

ElcoreNN DSP

Add fp16 support for Pad layer.
Increase performance of sigmoid by using precalculated table values.
Increase performance by fusing layers:

into:

X * Sigmoid(X) function uses precalculated table values.

onnx-elcorenn

Update to v1.3.3 version.
Save converter version to model json file.
Support python3.8.
Support Dropout and Cast layers.
Bug fixes.

ONNX Operators

AveragePool: 2D, dilations=1.
Clip.
Conv: support dimension=3 for fp32.
ConvTranspose: support dimension=1.
LSTM: reference version (fp32 only).
MatMul.

Keras layers

Conv1DTranspose: reference version (fp32).
Conv3D: reference version (fp32).
LSTM: reference version (fp32).

elcorenn-examples

Installation path has been changed to /usr/bin.
prepare-examples-env.sh has been renamed to elcorenn-examples-get-data.sh.

Examples look for models relative to $HOME directory:

Go into $HOME directory.
Run elcorenn-examples-get-data.sh script to get models.

1.4.0

Main changes

Add new layers support.
Add YOLOv5 example.
Update onnx-elcorenn converter to 1.3.0 version.
Update ElcoreNN CPU API.
Create web storage of converters and elcorenn-examples-data releases.

ElcoreNN CPU

Support for models with multiple inputs and outputs.
Support dma_buf as input of model.
Add functions to get name and shapes of model inputs or outputs.

ElcoreNN DSP

Add Div, Erf, Conv1D, Sqrt layers.

elcorenn-examples

Add new example of YOLOv5s usage.
Update archive with video and models.

onnx-elcorenn

Update onnx-elcorenn to 1.3.0 version.
Support models converted from Tensorflow to ONNX.
Support attribute sizes for Resize operator. It represents target size of the output tensor.

Compatibility: elcorenn == v1.4.0