Training fully connected networks with resistive memories: impact of device failures.

Hardware accelerators based on two-terminal non-volatile memories (NVMs) can potentially provide competitive speed and accuracy for the training of fully connected deep neural networks (FC-DNNs), with respect to GPUs and other digital accelerators. We recently proposed [S. Ambrogio et al.

Equivalent-accuracy accelerated neural-network training using analogue memory.

Neural-network training can be slow and energy intensive, owing to the need to transfer the weight data for the network between conventional digital memory chips and processor chips. Analogue non-volatile memory can accelerate the neural-network training algorithm known as backpropagation by performing parallelized multiply-accumulate operations in the analogue domain at the location of the weight data. However, the classification accuracies of such in situ training using non-volatile-memory hardware have generally been less than those of software-based training, owing to insufficient dynamic range and excessive weight-update asymmetry.

Case History Review of 2.467, Anal Fistulae Surgically Treated with, the Method of Arnous's French School.

Introduction: Considering the extensive experience developed in 28 years of medical practice in a specialist facility dedicated to proctological surgery and the treatment of 2.467 patients presenting with an anal fistula, the authors review problems associated with this disease from an aetiopathogenic, classifying, diagnostic, and therapeutic viewpoint.

Materials And Methods: The surgical treatment of Arnous's French School was adopted.