BNN algorithms - software

BNN algorithms

2019 Back to Simplicity: How to Train Accurate BNNs from Scratch? Joseph Bethge

non-idealities and noise

Committee machines—a universal method to deal with non-idealities in memristor-based neural networks Joksas, A. J. Kenyon & A. Mehonic

2019 Stochastic Computing for Hardware Implementation of Binarized Neural Networks

We evidence that the training procedure should be adapted for use with stochastic computing. Finally, the ASIC implementation of our scheme is investigated, in a system that closely associates logic and memory, implemented by Spin Torque Magnetoresistive Random Access Memory. This analysis shows that the stochastic computing approach can allow considerable savings with regards to conventional Binarized Neural networks in terms of area (62% area reduction on the Fashion-MNIST task)

• Improving Noise Tolerance of Mixed-Signal Neural Networks Michael Klachko, Mohammad Reza Mahmoodi, Dmitri B. Strukov

We apply various methods to improve noise robustness of the network and demonstrate an effective way to optimize useful
signal ranges through adaptive signal clipping

training

2020 Accurate deep neural network inference using computational phase-change memory Vinay Joshi

....However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the analog resistive memory devices will not result in significant loss of accuracy. Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in no appreciable accuracy loss when transferring weights to phase-change memory (PCM) devices. We also propose a compensation technique that exploits the batch normalization parameters