Resilient neural network training for accelerators with computing errors

—With the advancements of neural networks, customized accelerators are increasingly adopted in massive AI applications. To gain higher energy efficiency or performance, many hardware design optimizations such as near-threshold logic or overclocking can be utilized. In these cases, computing errors may happen and the computing errors are difficult to be captured by conventional training on general purposed processors (GPPs). Applying the offline trained neural network models to the accelerators with errors directly may lead to considerable prediction accuracy loss. To address this problem, we explore the resilience of neural network models and relax the accelerator design constraints to enable aggressive design options. First of all, we propose to train the neural network models using the accelerators’ forward computing results such that the models can learn both the data and the computing errors. In addition, we observe that some of the neural network layers are more sensitive to the computing errors. With this observation, we schedule the most sensitive layer to the attached GPP to reduce the negative influence of the computing errors. According to the experiments, the neural network models obtained from the proposed training outperform the original models significantly when the CNN accelerators are affected by computing errors

Skills, social insurance, and changes in innovation investment after the onset of the financial crisis in Europe

This paper compares investments in innovation from the early days of the financial crisis up to mid 2009 using a survey covering more than 5,000 firms across twenty one European countries. Our interest is in how differences in labour market institutions and human capital affect a firm’s innovation investment during the recent financial crisis. We find that continuity of investment in innovation in Europe during the onset of the financial crisis in 2008-9 was strongest in countries which have both high earnings replacement rates and high participation in vocational education and training; countries with just one were more likely to see reduced innovation, while we find no effect (either positive or negative) from job security

High-Throughput Classification of Radiographs Using Deep Convolutional Neural Networks.

The study aimed to determine if computer vision techniques rooted in deep learning can use a small set of radiographs to perform clinically relevant image classification with high fidelity. One thousand eight hundred eighty-five chest radiographs on 909 patients obtained between January 2013 and July 2015 at our institution were retrieved and anonymized. The source images were manually annotated as frontal or lateral and randomly divided into training, validation, and test sets. Training and validation sets were augmented to over 150,000 images using standard image manipulations. We then pre-trained a series of deep convolutional networks based on the open-source GoogLeNet with various transformations of the open-source ImageNet (non-radiology) images. These trained networks were then fine-tuned using the original and augmented radiology images. The model with highest validation accuracy was applied to our institutional test set and a publicly available set. Accuracy was assessed by using the Youden Index to set a binary cutoff for frontal or lateral classification. This retrospective study was IRB approved prior to initiation. A network pre-trained on 1.2 million greyscale ImageNet images and fine-tuned on augmented radiographs was chosen. The binary classification method correctly classified 100 % (95 % CI 99.73-100 %) of both our test set and the publicly available images. Classification was rapid, at 38 images per second. A deep convolutional neural network created using non-radiological images, and an augmented set of radiographs is effective in highly accurate classification of chest radiograph view type and is a feasible, rapid method for high-throughput annotation

Інноваційні моделі навчання і підготовки кадрів для індустрії високих технологій в Україні

The problems of development of innovative learning environment of continuous education and training of skilled personnel for high-tech industry are described. Aspects of organization of ICT based learning environment of vocational and technical school on the basis of cloud computing and outsourcing are revealed. The three-stage conceptual model for perspective education and training of workers for high-tech industries is proposed. The model of cloud-based solution for design of learning environment for vocational education and training of skilled workers is introduced.У статті висвітлено проблеми розвитку інноваційного середовища навчання, неперервної освіти і підготовки кадрів для високотехнологічних галузей промисловості. Виявлено особливості організації інформаційно-освітнього середовища професійно-технічних навчальних закладів на основі технології хмарних обчислень і механізму аутсорсингу. Запропонована триступенева концептуальна модель навчання та підготовки кадрів для високотехнологічних галузей виробництва. Обґрунтовано моделі хмарних рішень для проектування середовища навчання для професійної освіти і підготовки високо кваліфікованих робітникі