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CHARLES: A C++ fixed-point library for Photonic-Aware Neural Networks.

Emilio Paolini1, Lorenzo De Marinis2, Luca Maggiani3

  • 1Scuola Superiore Sant'Anna, Pisa, 56124, Italy; National Research Council of Italy - Institute of Electronics, Information Engineering and Telecommunications (CNR-IEIIT), Pisa, 56122, Italy; Sma-RTy Italia Srl, Carugate, 20061, Italy.

Neural Networks : the Official Journal of the International Neural Network Society
|March 29, 2023
PubMed
Summary

We introduce CHARLES, a C++ library for simulating Photonic-Aware Neural Networks (PANNs). Fixed-point training significantly improves PANN inference accuracy on low bitwidths compared to floating-point training.

Keywords:
C++ libraryFixed-point training/inferenceHardware acceleratorsPhotonic Aware Neural NetworksPhotonic neuromorphic computing

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Photonics

Background:

  • Photonic-Aware Neural Networks (PANNs) must account for hardware constraints, particularly low numerical precision.
  • Simulating PANNs requires tools that handle low-precision computations.

Purpose of the Study:

  • To present CHARLES, a C++ library for simulating PANNs.
  • To compare the impact of fixed-point versus floating-point training on PANN inference accuracy under quantization.

Main Methods:

  • Developed CHARLES, a C++ library supporting fixed-point inference and both floating-point/fixed-point training.
  • Evaluated CHARLES using Iris, MNIST, and Fashion-MNIST datasets.
  • Compared accuracy loss from quantization after floating-point training versus fixed-point training.

Main Results:

  • Fixed-point training outperforms floating-point training for PANN inference on low bitwidths.
  • Floating-point training followed by quantization leads to significant accuracy loss.
  • Fixed-point training minimizes accuracy loss, achieving 90.4% (MNIST) and 68.1% (Fashion-MNIST) at 6 bits.

Conclusions:

  • Fixed-point training is crucial for effective PANN deployment on hardware with limited precision.
  • CHARLES provides a validated tool for PANN simulation and numerical format optimization.