An Analytical Model of Configurable Systolic Arrays to find the Best-Fitting Accelerator for a given DNN Workload

Since their breakthrough, complexity of Deep Neural Networks (DNNs) is rising steadily. As a result, accelerators for DNNs are now used in many domains. However, designing and configuring an accelerator that meets the requirements of a given application perfectly is a challenging task. In this paper, we therefore present our approach to support the accelerator design process. With an analytical model of a systolic array we can estimate performance, energy consumption and area for each design option. To determine these metrics, usually a cycle accurate simulation is performed, which is a time-consuming task. Hence, the design space has to be restricted heavily. Analytical modelling, however, allows for fast evaluation of a design using a mathematical abstraction of the accelerator. For DNNs, this works especially well since the dataflow and memory accesses have high regularity. To show the correctness of our model, we perform an exemplary realization with the state-of-the-art systolic array generator Gemmini and compare it with a cycle accurate simulation and state-of-the-art modelling tools, showing less than 1% deviation. We also conducted a design space exploration, showing the analytical model’s capabilities to support an accelerator design. In a case study on ResNet-34, we can demonstrate that our model and DSE tool reduces the time to find the best-fitting solution by four or two orders of magnitude compared to a cycle-accurate simulation or state-of-the-art modelling tools, respectively

EFFECT: An End-to-End Framework for Evaluating Strategies for Parallel AI Anomaly Detection

Neural networks achieve high accuracy in tasks like image recognition or segmentation. However, their application in safety-critical domains is limited due to their black-box nature and vulnerability to specific types of attacks. To mitigate this, methods detecting out-of-distribution or adversarial attacks in parallel to the network inference were introduced. These methods are hard to compare because they were developed for different use cases, datasets, and networks. To fill this gap, we introduce EFFECT, an end-to-end framework to evaluate and compare new methods for anomaly detection, without the need for retraining and by using traces of intermediate inference results. The presented workflow works with every preexisting neural network architecture and evaluates the considered anomaly detection methods in terms of accuracy and computational complexity. We demonstrate EFFECT\u27s capabilities, by creating new detectors for ShuffleNet and MobileNetV2 for anomaly detection as well as fault origin detection. EFFECT allows us to design an anomaly detector, based on the Mahalanobis distance as well as CNN based detectors. For both use cases, we achieve accuracies of over 85 %, classifying inferences as normal or abnormal, and thus beating existing methods

An Individualized, Less-Invasive Surgical Approach Algorithm Improves Outcome in Elderly Patients Undergoing Mitral Valve Surgery

Background: For mitral valve surgery (MVS) in elderly, frail patients with increasing life expectancy, finding the least harmful means of access is a challenge. In the complexity of MVS approach evolution, using three different approaches (mini-thoracotomy (MT), partial upper-sternotomy (PS), full-sternotomy (FS), we developed a personalized, minimized-invasiveness algorithm for MVS. Methods: In this retrospective analysis, 517 elderly patients (≥70 years) were identified who had undergone MVS ± TV repair. MVS was performed via MT (n = 274), FS (n = 128) and PS (n = 115). The appropriate access type was defined according to several clinical patient conditions. Using uni- and multivariate regression models, we analyzed combined operative success (residual MV regurgitation, conversion to MV replacement or larger thoracic incisions); perioperative success (30-days mortality, thoracotomy, ECMO, pacemaker implantation, dialysis, longer ventilation); and reoperation-free long-term survival. An additional EuroSCORE2 adjustment was performed to reduce the bias of clinical conditions between all access types. Results: The EuroSCORE2-adjusted Cox regression analysis showed significantly increased reoperation-free survival in the MT cohort compared to FS (HR 0.640; 95% CI 0.442–0.926; p = 0.018). Mortality was additionally reduced after the implementation of PS (p = 0.023). Combined operative success was comparable between the three access types. The perioperative success was higher in the MT cohort compared to FS (OR 2.19, 95% CI 1.32–3.63; p = 0.002). Conclusion: Less-invasive approaches in elderly patients improve perioperative success and reoperation-free survival in those undergoing MVS procedures

Impaired immune response mediated by prostaglandin E2 promotes severe COVID-19 disease

The SARS-CoV-2 coronavirus has led to a pandemic with millions of people affected. The present study finds that risk-factors for severe COVID-19 disease courses, i.e. male sex, older age and sedentary life style are associated with higher prostaglandin E2 (PGE2) serum levels in blood samples from unaffected subjects. In COVID-19 patients, PGE2 blood levels are markedly elevated and correlate positively with disease severity. SARS-CoV-2 induces PGE2 generation and secretion in infected lung epithelial cells by upregulating cyclo-oxygenase (COX)-2 and reducing the PG-degrading enzyme 15-hydroxyprostaglan- din-dehydrogenase. Also living human precision cut lung slices (PCLS) infected with SARS- CoV-2 display upregulated COX-2. Regular exercise in aged individuals lowers PGE2 serum levels, which leads to increased Paired-Box-Protein-Pax-5 (PAX5) expression, a master regulator of B-cell survival, proliferation and differentiation also towards long lived memory B-cells, in human pre-B-cell lines. Moreover, PGE2 levels in serum of COVID-19 patients lowers the expression of PAX5 in human pre-B-cell lines. The PGE2 inhibitor Taxi- folin reduces SARS-CoV-2-induced PGE2 production. In conclusion, SARS-CoV-2, male sex, old age, and sedentary life style increase PGE2 levels, which may reduce the early anti-viral defense as well as the development of immunity promoting severe disease courses and multiple infections. Regular exercise and Taxifolin treatment may reduce these risks and prevent severe disease courses