Predicting Real-Time Neural Network Performance

Quantifying Performance

Timur Doumler shares more information on meeting real-time deadlines in audio programming.

What’s A Good Score?

Predicting Performance

  • Implement the relevant neural network layers (using SIMD instructions wherever possible).
  • Count the operations used by the neural network as a function of the network hyper-parameters.
  • Measure the network performance for a variety of hyper-parameter choices.
  • Use a regression to estimate how long each operation will take.

Example: Dense Network

Visualization of a Dense network with 2 inputs, 2 outputs, 2 hidden layers, and a hidden size of 8.
  • SIMD Multiplies + SIMD HSums + Scalar Adds: W * (N + L * W + O) / V
  • SIMD Adds: ((L + 1) * W + O) / V
  • SIMD Activations: (L + 1) * W / V
  • SIMD Multiplies + SIMD HSums + Scalar Adds: 5.45500463e-03 seconds
  • SIMD Adds: 1.47277176e-25 seconds
  • SIMD ReLU Activations: 7.23480113e-26
Real-Time Factor for Dense/ReLU and Dense/Tanh networks of a given size. Networks that fall above the red line are too slow to run in real-time at 48 kHz.

Example: Recurrent Networks

Real-Time Factor for LSTM and GRU networks of a given size.

Why Is This Useful?

Conclusion

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Jatin Chowdhury is a student.

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Jatin Chowdhury

Jatin Chowdhury

Jatin Chowdhury is a student.

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