DLAS: An Exploration and Assessment of the Deep Learning Acceleration Stack

Deep Neural Networks (DNNs) are extremely computationally demanding, which presents a large barrier to their deployment on resource-constrained devices. Since such devices are where many emerging deep learning applications lie (e.g., drones, vision-based medical technology), significant bodies of work from both the machine learning and systems communities have attempted to provide optimizations to accelerate DNNs. To help unify these two perspectives, in this paper we combine machine learning and systems techniques within the Deep Learning Acceleration Stack (DLAS), and demonstrate how these layers can be tightly dependent on each other with an across-stack perturbation study. We evaluate the impact on accuracy and inference time when varying different parameters of DLAS across two datasets, seven popular DNN architectures, four DNN compression techniques, three algorithmic primitives with sparse and dense variants, untuned and auto-scheduled code generation, and four hardware platforms. Our evaluation highlights how perturbations across DLAS parameters can cause significant variation and across-stack interactions. The highest level observation from our evaluation is that the model size, accuracy, and inference time are not guaranteed to be correlated. Overall we make 13 key observations, including that speedups provided by compression techniques are very hardware dependent, and that compiler auto-tuning can significantly alter what the best algorithm to use for a given configuration is. With DLAS, we aim to provide a reference framework to aid machine learning and systems practitioners in reasoning about the context in which their respective DNN acceleration solutions exist in. With our evaluation strongly motivating the need for co-design, we believe that DLAS can be a valuable concept for exploring the next generation of co-designed accelerated deep learning solutions

Optimizing Grouped Convolutions on Edge Devices

When deploying a deep neural network on constrained hardware, it is possible to replace the network's standard convolutions with grouped convolutions. This allows for substantial memory savings with minimal loss of accuracy. However, current implementations of grouped convolutions in modern deep learning frameworks are far from performing optimally in terms of speed. In this paper we propose Grouped Spatial Pack Convolutions (GSPC), a new implementation of grouped convolutions that outperforms existing solutions. We implement GSPC in TVM, which provides state-of-the-art performance on edge devices. We analyze a set of networks utilizing different types of grouped convolutions and evaluate their performance in terms of inference time on several edge devices. We observe that our new implementation scales well with the number of groups and provides the best inference times in all settings, improving the existing implementations of grouped convolutions in TVM, PyTorch and TensorFlow Lite by 3.4x, 8x and 4x on average respectively. Code is available at https://github.com/gecLAB/tvm-GSPC/Comment: Camera ready version to be published at ASAP 2020 - The 31st IEEE International Conference on Application-specific Systems, Architectures and Processors. 8 pages, 6 figure

Penetrance of Hypertrophic Cardiomyopathy in Sarcomere Protein Mutation Carriers

[Abstract] Background. Predictive genetic screening of relatives of patients with hypertrophic cardiomyopathy (HCM) caused by sarcomere protein (SP) gene mutations is current standard of care, but there are few data on long-term outcomes in mutation carriers without HCM. Objectives. The aim of this study was to determine the incidence of new HCM diagnosis in SP mutation carriers. Methods. This was a retrospective analysis of adult and pediatric SP mutation carriers identified during family screening who did not fulfill diagnostic criteria for HCM at first evaluation. Results. The authors evaluated 285 individuals from 156 families (median age 14.2 years [interquartile range: 6.8 to 31.6 years], 141 [49.5%] male individuals); 145 (50.9%) underwent cardiac magnetic resonance (CMR). Frequency of causal genes was as follows: MYBPC3 n = 123 (43.2%), MYH7 n = 69 (24.2%), TNNI3 n = 39 (13.7%), TNNT2 n = 34 (11.9%), TPM1 n = 9 (3.2%), MYL2 n = 6 (2.1%), ACTC1 n = 1 (0.4%), multiple mutations n = 4 (1.4%). Median follow-up was 8.0 years (interquartile range: 4.0 to 13.3 years) and 86 (30.2%) patients developed HCM; 16 of 50 (32.0%) fulfilled diagnostic criteria on CMR but not echocardiography. Estimated HCM penetrance at 15 years of follow-up was 46% (95% confidence interval [CI]: 38% to 54%). In a multivariable model adjusted for age and stratified for CMR, independent predictors of HCM development were male sex (hazard ratio [HR]: 2.91; 95% CI: 1.82 to 4.65) and abnormal electrocardiogram (ECG) (HR: 4.02; 95% CI: 2.51 to 6.44); TNNI3 variants had the lowest risk (HR: 0.19; 95% CI: 0.07 to 0.55, compared to MYBPC3). Conclusions. Following a first negative screening, approximately 50% of SP mutation carriers develop HCM over 15 years of follow-up. Male sex and an abnormal ECG are associated with a higher risk of developing HCM. Regular CMR should be considered in long-term screening

Estimating the burden of heat illness in England during the 2013 summer heatwave using syndromic surveillance.

BACKGROUND: The burden of heat illness on health systems is not well described in the UK. Although the UK generally experiences mild summers, the frequency and intensity of hot weather is likely to increase due to climate change, particularly in Southern England. We investigated the impact of the moderate heatwave in 2013 on primary care and emergency department (ED) visits using syndromic surveillance data in England. METHODS: General practitioner in hours (GPIH), GP out of hours (GPOOH) and ED syndromic surveillance systems were used to monitor the health impact of heat/sun stroke symptoms (heat illness). Data were stratified by age group and compared between heatwave and non-heatwave years. Incidence rate ratios were calculated for GPIH heat illness consultations. RESULTS: GP consultations and ED attendances for heat illness increased during the heatwave period; GPIH consultations increased across all age groups, but the highest rates were in school children and those aged ≥75 years, with the latter persisting beyond the end of the heatwave. Extrapolating to the English population, we estimated that the number of GPIH consultations for heat illness during the whole summer (May to September) 2013 was 1166 (95% CI 1064 to 1268). This was double the rate observed during non-heatwave years. CONCLUSIONS: These findings support the monitoring of heat illness (symptoms of heat/sun stroke) as part of the Heatwave Plan for England, but also suggest that specifically monitoring heat illness in children, especially those of school age, would provide additional early warning of, and situation awareness during heatwaves

Arrhythmic Risk Stratification in Arrhythmogenic Right Ventricular Cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is an heritable cardiomyopathy characterized by a predominantly arrhythmic presentation. It represents the leading cause of sudden cardiac death (SCD) among athletes and poses a significant morbidity treat in the general population. As a causative treatment for ARVC is still not available, the placement of an implantable cardioverter defibrillator (ICD) represent the current cornerstone for SCD prevention in this setting. Thanks to international ARVC-dedicated efforts, significant steps have been achieved in recent years towards an individualized, patient-centered risk stratification approach. A novel risk calculator algorithm estimating the 5 year risk of arrhythmias of patients with ARVC have been introduced in clinical practice and subsequently validated. The purpose of this article is to summarize the body of evidence that has allowed the development of this tool and to discuss the best way to implement its use in the care of an individual patient

Arrhythmic risk stratification in arrhythmogenic right ventricular cardiomyopathy

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable cardiomyopathy characterized by a predominantly arrhythmic presentation. It represents the leading cause of sudden cardiac death (SCD) among athletes and poses a significant morbidity threat in the general population. As a causative treatment for ARVC is still not available, the placement of an implantable cardioverter defibrillator represents the current cornerstone for SCD prevention in this setting. Thanks to international ARVC-dedicated efforts, significant steps have been achieved in recent years towards an individualized, patient-centred risk stratification approach. A novel risk calculator algorithm estimating the 5-year risk of arrhythmias of patients with ARVC has been introduced in clinical practice and subsequently validated. The purpose of this article is to summarize the body of evidence that has allowed the development of this tool and to discuss the best way to implement its use in the care of an individual patient

Engineered living photosynthetic biocomposites for intensified biological carbon capture

Carbon capture and storage is required to meet Paris Agreement targets. Photosynthesis is nature’s carbon capture technology. Drawing inspiration from lichen, we engineered 3D photosynthetic cyanobacterial biocomposites (i.e., lichen mimics) using acrylic latex polymers applied to loofah sponge. Biocomposites had CO2 uptake rates of 1.57 ± 0.08 g CO2 g−1biomass d−1. Uptake rates were based on the dry biomass at the start of the trial and incorporate the CO2 used to grow new biomass as well as that contained in storage compounds such as carbohydrates. These uptake rates represent 14–20-fold improvements over suspension controls, potentially scaling to capture 570 tCO2 t−1biomass yr−1, with an equivalent land consumption of 5.5–8.17 × 106 ha, delivering annualized CO2 removal of 8–12 GtCO2, compared with 0.4–1.2 × 109 ha for forestry-based bioenergy with carbon capture and storage. The biocomposites remained functional for 12 weeks without additional nutrient or water supplementation, whereupon experiments were terminated. Engineered and optimized cyanobacteria biocomposites have potential for sustainable scalable deployment as part of humanity’s multifaceted technological stand against climate change, offering enhanced CO2 removal with low water, nutrient, and land use penalties

Students' Perception of the Psycho-Social Clinical Learning Environment: An Evaluation of Placement Models

Nursing is a practice based discipline. A supportive environment has been identified as important for the transfer of learning in the clinical context. The aim of the paper was to assess undergraduate nurses' perceptions of the psychosocial characteristics of clinical learning environments within three different clinical placement models. Three hundred and eight-nine undergraduate nursing students rated their perceptions of the psycho-social learning environment using a Clinical Learning Environment Inventory. There were 16 respondents in the Preceptor model category, 269 respondents in the Facilitation model category and 114 respondents in the clinical education unit model across 25 different clinical areas in one tertiary facility. The most positive social climate was associated with the preceptor model. On all subscales the median score was rated higher than the two other models. When clinical education units were compared with the standard facilitation model the median score was rated higher in all of the subscales in the Clinical Learning Environment Inventory. These results suggest that while preceptoring is an effective clinical placement strategy that provides psycho-social support for students, clinical education units that are more sustainable through their placement of greater numbers of students, can provide greater psycho-social support for students than traditional models

Immobilising Microalgae and Cyanobacteria as Biocomposites: New Opportunities to Intensify Algae Biotechnology and Bioprocessing

There is a groundswell of interest in applying phototrophic microorganisms, specifically microalgae and cyanobacteria, for biotechnology and ecosystem service applications. However, there are inherent challenges associated with conventional routes to their deployment (using ponds, raceways and photobioreactors) which are synonymous with suspension cultivation techniques. Cultivation as biofilms partly ameliorates these issues; however, based on the principles of process intensification, by taking a step beyond biofilms and exploiting nature inspired artificial cell immobilisation, new opportunities become available, particularly for applications requiring extensive deployment periods (e.g., carbon capture and wastewater bioremediation). We explore the rationale for, and approaches to immobilised cultivation, in particular the application of latex-based polymer immobilisation as living biocomposites. We discuss how biocomposites can be optimised at the design stage based on mass transfer limitations. Finally, we predict that biocomposites will have a defining role in realising the deployment of metabolically engineered organisms for real world applications that may tip the balance of risk towards their environmental deployment