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Test running ONNX models on Bela via ROOT@CERN's SOFIE inference code generator


Code generation for fast inference of Deep Learning models ROOT/TMVA SOFIE (“System for Optimized Fast Inference code Emit”) is a new package introduced in this release that generates C++ functions easily invokable for the fast inference of trained neural network models. It takes ONNX model files as inputs and produces C++ header files that can be included and utilized in a “plug-and-go” style. This is a new development and it is currently still in experimental stage. SOFIE can take your trained ONNX model and generate blazingly fast C++ code from it, depending only on BLAS.

SOFIE installation

Requires building ROOT from source with experimental flags:

git clone

cd root

mkdir root_install root_build

cd root_build

cmake -DCMAKE_INSTALL_PREFIX=../root_install ../ -Dtmva-sofie=ON -Dtmva-pymva=On -DPython3_EXECUTABLE=CHOOSEYOURPYTHONBINARY

cmake --build . --target install -j2

source <installdir>/bin/

Bela results 2022-04-07

Requires Eigen library to be installed on Bela.

I installed via apt-get install libeigen3-dev but for some reason could only include it by editing the model files with #include "../../../usr/include/eigen3/Eigen/Eigen" :/.

ONNX Time (us) CPU % Notes
Linear_2 650 9.3
Linear_4 720 9.5
Linear_8 920 10
Linear_16 1480 10.3
Linear_32 segfault
Linear_64 No .hxx file available
Linear_event undefined reference to `sgemv_'
Linear_RDF undefined reference to `sgemv_'
LinearNN segfault

Tried with this:

And got ~570ms per pass, apparently 25x slower than ArmNN!

#include <Bela.h>
#include "Linear_2.hxx"
// #include "Linear_4.hxx"
// #include "Linear_8.hxx"
// #include "Linear_16.hxx"
// #include "Linear_32.hxx"
// #include "Linear_event.hxx"
// #include "Linear_RDF.hxx"
// #include "LinearNN.hxx"
#include <algorithm>
#include <iostream>
#include <chrono>
#include <stdlib.h>
#include <time.h>
#include <cmath>
#include <numeric>
int INPUT_SIZE = 2048;
float input[2048];
std::vector<float> out;
AuxiliaryTask sofieTask;
void sofieProcess (void*);
float sofieInterval = 5; // seconds
int sofieCount = 0;
int sofieProcessCount = 0;
int sofieProcessSkip = 5; // ignore first few runs for mean calculation
std::vector<float> total_time;
bool setup(BelaContext *context, void *userData)
for (int i = 0; i < INPUT_SIZE; ++i)
input[i] = rand();
sofieTask = Bela_createAuxiliaryTask(sofieProcess, 50, "sofie-task");
sofieInterval = (int) sofieInterval * 44100;
return true;
void render(BelaContext *context, void *userData)
for(unsigned int n = 0; n < context->audioFrames; n++) {
if(++sofieCount >= sofieInterval) {
sofieCount = 0;
void sofieProcess (void*)
if (++sofieProcessCount >= sofieProcessSkip)
auto t1 = std::chrono::high_resolution_clock::now();
out = TMVA_SOFIE_Linear_2::infer(input);
// out = TMVA_SOFIE_Linear_4::infer(input);
// out = TMVA_SOFIE_Linear_8::infer(input);
// out = TMVA_SOFIE_Linear_16::infer(input);
// out = TMVA_SOFIE_Linear_32::infer(input);
// out = TMVA_SOFIE_Linear_event::infer(input);
// float rdf = TMVA_SOFIE_Linear_RDF::infer(rand(), rand(), rand());
// out = TMVA_SOFIE_LinearNN::infer(input);
auto t2 = std::chrono::high_resolution_clock::now();
auto duration = float(std::chrono::duration_cast<std::chrono::microseconds>( t2 - t1 ).count());
float sum = std::accumulate(total_time.begin(), total_time.end(), 0.0);
float mean = sum / total_time.size();
float sq_sum = std::inner_product(total_time.begin(), total_time.end(), total_time.begin(), 0.0);
float std = std::sqrt(sq_sum / total_time.size() - mean * mean);
printf("sofieProcess() Total Runs: %i, This Run: %.f us, Mean: %.f us, Std: %.f us\n", sofieProcessCount-sofieProcessSkip, duration, mean, std);
} else {
printf("sofieProcess() Total Runs: %i (skipping first %i)\n", sofieProcessCount, sofieProcessSkip);
void cleanup(BelaContext *context, void *userData)
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