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., Nature, 2018] novel neuromorphic crossbar arrays, consisting of a pair of phase-change memory (PCM) devices combined with a pair of 3-Transistor 1-Capacitor (3T1C) circuit elements, so that each weight was implemented using multiple conductances of varying significance, and then showed that this weight element can train FC-DNNs to software-equivalent accuracies. Unfortunately, however, real arrays of emerging NVMs such as PCM typically include some failed devices (e.g., <100% yield), either due to fabrication issues or early endurance failures, which can degrade DNN training accuracy. This paper explores the impact of device failures, NVM conductances that may contribute read current but which cannot be programmed, on DNN training and test accuracy. Results show that "stuck-on" and "dead" devices, exhibiting high and low read conductances, respectively, do in fact degrade accuracy performance to some degree. We find that the presence of the CMOS-based and thus highly-reliable 3T1C devices greatly increase system robustness. After studying the inherent mechanisms, we study the dependence of DNN accuracy on the number of functional weights, the number of neurons in the hidden layer, and the number and type of damaged devices. Finally, we describe conditions under which making the network larger or adjusting the network hyperparameters can still improve the network accuracy, even in the presence of failed devices.

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. Here we demonstrate mixed hardware-software neural-network implementations that involve up to 204,900 synapses and that combine long-term storage in phase-change memory, near-linear updates of volatile capacitors and weight-data transfer with 'polarity inversion' to cancel out inherent device-to-device variations. We achieve generalization accuracies (on previously unseen data) equivalent to those of software-based training on various commonly used machine-learning test datasets (MNIST, MNIST-backrand, CIFAR-10 and CIFAR-100). The computational energy efficiency of 28,065 billion operations per second per watt and throughput per area of 3.6 trillion operations per second per square millimetre that we calculate for our implementation exceed those of today's graphical processing units by two orders of magnitude. This work provides a path towards hardware accelerators that are both fast and energy efficient, particularly on fully connected neural-network layers.

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. The method envisions slow sectioning of the sphincter by means of elastic constriction, even dividing surgical sessions. RESULTS: Results were excellent, recording 99.5% of complete healings, while failures and complications numbered 0.3% of incomplete healings, 0.2% of relapses, 2.8% of soiling, and 1.4% of transitory gas incontinence. CONCLUSIONS: Correct diagnosis of the type of fistula, the choice of a perfect surgical technique, and thorough long-term follow-up of the postoperative progress of surgical wounds are the basic premises to achieve the patient's healing.