Medications prescriptions in COVID-19 pregnant and lactating women: the Bergamo Teratology Information Service experience during COVID-19 outbreak in Italy

Abstract Objectives The severe acute respiratory syndrome coronavirus 2 (COVID-19) outbreak in Italy, especially in Lombardy and Bergamo city, represented probably nowadays one of the first major clusters of COVID-19 in the world. The aim of this report is to describe the activity of Bergamo Teratology Information Service (TIS) in supporting the public and health-care personnel in case of drug prescriptions in suspected/confirmed COVID-19 pregnant and lactating patients during COVID-19 outbreak in Italy. Methods All Bergamo TIS requests concerning COVID-19 pregnant and lactating women have been retrospectively evaluated from 1 March to 15 April 2020. Type of medications, drug's safety profile and compatibility with pregnancy and lactation are reported. Results Our service received information calls concerning 48 (9 pregnant, 35 lactating) patients. Among pregnant and lactating women, the requests of information were related to 16 and 60 drugs prescriptions respectively. More than half concerned drugs prescriptions during the first and second trimester (13/16) and during the first six months of lactation (37/60). Hydroxychloroquine and azithromycin were the most involved. Conclusions Hydroxychloroquine and azithromycin at dosages used for COVID-19 may be considered compatible and reasonably safe either in pregnancy and lactation. Antivirals may be considered acceptable in pregnancy. During lactation lopinavir and ritonavir probably exhibit some supportive data from literature that darunavir and cobicistat do not. Tocilizumab may be considered for COVID-19 treatment because no increased malformation rate were observed until now. However caution may be advised because human data are limited and the potential risk of embryo-fetal toxicity cannot be excluded

On How to Design Dataflow FPGA-Based Accelerators for Convolutional Neural Networks

In the past few years we have experienced an extremely rapid growth of modern applications based on deep learning algorithms such as Convolutional Neural Network (CNN), and consequently, an intensification of academic and industrial research focused on the optimization of their imple- mentation. Among the different alternatives that have been ex- plored, FPGAs seems to be one of the most attractive, as they are able to deliver high performance and energy-efficiency, thanks to their inherent parallelism and direct hardware execution, while retaining extreme flexibility due to their reconfigurability.In this paper we present a design methodology of a dataflow accelerator for the implementation of CNNs on FPGAs, that ensures scalability - and achieve a higher degree of parallelism as the size of the CNN increases - and an efficient exploitation of the available resources. Furthermore, we analyze resource consumption of the layers of the CNN as well as latency in relation to the implementation's hyperparameters. Finally, we show that the proposed design implements a high-level pipeline between the different network layers, and as a result, we can improve the latency to process an image by feeding the CNN with batches of multiple images

A framework with cloud integration for CNN acceleration on FPGA devices

The recent years have seen a rapid diffusion of deep learning algorithms as Convolutional Neural Networks (CNNs), and as a consequence, an intensification of industrial and academic research focused on optimizing their implementation. Different computing architectures have been explored, and among all of them, FPGAs seem to be a very attractive choice, since they can deliver sustained performance with high power efficiency, as CNNs can be directly mapped onto hardware, and still offer flexibility thanks to their programmability. In this paper, we present an end-to-end framework to implement CNNs using a dataflow acceleration methodology. The resulting spatial accelerator can be scaled in size if enough resources are available and can exploit both intra- and inter-layers parallelism. We integrate the proposed framework with the deep learning engine Caffe, meaning that we are able to generate the accelerator starting from a Caffe model. We also provide cloud integration of such framework, enabling users to synthesize and deploy the accelerator on the Amazon F1 instances

A pipelined and scalable dataflow implementation of convolutional neural networks on FPGA

Convolutional Neural Network (CNN) is a deep learning algorithm extended from Artificial Neural Network (ANN) and widely used for image classification and recognition, thanks to its invariance to distortions. The recent rapid growth of applications based on deep learning algorithms, especially in the context of Big Data analytics, has dramatically improved both industrial and academic research and exploration of optimized implementations of CNNs on accelerators such as GPUs, FPGAs and ASICs, as general purpose processors can hardly meet the ever increasing performance and energy-efficiency requirements. FPGAs in particular are one of the most attractive alternative, as they allow the exploitation of the implicit parallelism of the algorithm and the acceleration of the different layers of a CNN with custom optimizations, while retaining extreme flexibility thanks to their reconfigurability. In this work, we propose a methodology to implement CNNs on FPGAs in a modular, scalable way. This is done by exploiting the dataflow pattern of convolutions, using an approach derived from previous work on the acceleration of Iterative Stencil Loops (ISLs), a computational pattern that shares some characteristics with convolutions. Furthermore, this approach allows the imple- mentation of a high-level pipeline between the different network layers, resulting in an increase of the overall performance when the CNN is employed to process batches of multiple images, as it would happen in real-life scenarios