MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems

Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost, multi-accelerator systems widely exist in data centers, cloud platforms, and SoCs. Thus, a challenging problem arises in multi-accelerator systems: selecting a proper combination of accelerators from available designs and searching for efficient DNN mapping strategies. To this end, we propose MARS, a novel mapping framework that can perform computation-aware accelerator selection, and apply communication-aware sharding strategies to maximize parallelism. Experimental results show that MARS can achieve 32.2% latency reduction on average for typical DNN workloads compared to the baseline, and 59.4% latency reduction on heterogeneous models compared to the corresponding state-of-the-art method.Comment: Accepted by 60th DA

Virtual carbon and water flows embodied in global fashion trade - a case study of denim products

The environmental impacts of the fashion industry have been aroused wide concerns. The globalization and fragmentation of the textile and fashion system have led to the uneven distribution of environmental consequences. As denim is the fabric of jeans that is representative of fashion, this study assessed virtual carbon and water flows embodied in the global denim-product trade, and footprints of denim production were quantified by life-cycle assessment and water footprint assessment. Results indicated that virtual carbon embodied in the global denim trade increased obviously from 14.8 Mt CO2e in 2001 to 16.0 Mt CO2e in 2018, and the virtual water consumption dropped from 5.6 billion m3 to 4.7 billion m3 from 2001 to 2018. The denim fabric production and cotton fibre production respectively contribute the most of the carbon emissions and water consumption. Polyester blended denim has 5% larger carbon footprint and 72% lower water footprint than cotton denim, and contributes to increasing embodied carbon emissions (from 4% in 2001 to 43% in 2018). Increasing the utilization of polyester blended denim would save water but face more pressures on carbon emission reduction. In the past two decades, virtual carbon and water flows embodied in the global denim trade are relocating, main jean consumers (i.e., the USA, EU-15, and Japan) withdraw the denim manufacturing supply chain and developing countries (i.e., China, India, and Pakistan) with higher carbon and water footprint undertake main global denim production, facing increasing climate-related risks and water crisis. The South-South cooperation helps share successful experiences, save production cost, and lessen resource consumption and environmental emissions. The production and consumption of denim should be shifted to circular and sustainable ways and new business models are required. The analysis framework can provide the basis for exploring environmental flows of product-level trade, and results can offer a basis for environmental policies and control strategies of the fashion industry, and as well as the sustainable production and consumption of garment

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research

The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research

Block-Skim: Efficient Question Answering for Transformer

Transformer models have achieved promising results on natural language processing (NLP) tasks including extractive question answering (QA). Common Transformer encoders used in NLP tasks process the hidden states of all input tokens in the context paragraph throughout all layers. However, different from other tasks such as sequence classification, answering the raised question does not necessarily need all the tokens in the context paragraph. Following this motivation, we propose Block-skim, which learns to skim unnecessary context in higher hidden layers to improve and accelerate the Transformer performance. The key idea of Block-Skim is to identify the context that must be further processed and those that could be safely discarded early on during inference. Critically, we find that such information could be sufficiently derived from the self-attention weights inside the Transformer model. We further prune the hidden states corresponding to the unnecessary positions early in lower layers, achieving significant inference-time speedup. To our surprise, we observe that models pruned in this way outperform their full-size counterparts. Block-Skim improves QA models' accuracy on different datasets and achieves 3 times speedup on BERT-base model

Characterization, in Vitro and in Vivo Evaluation of Naringenin-Hydroxypropyl-β-Cyclodextrin Inclusion for Pulmonary Delivery

Naringenin, a flavonoid compound which exists abundantly in Citrus fruits, is proven to possess excellent antitussive and expectorant effects. However, the clinical applications of naringenin are restricted by its poor solubility and low local concentration by oral administration. The aim of the present study is to prepare a naringenin-hydroxypropyl-β-cyclodextrin (naringenin-HPβCD) inclusion as an inhalation solution for pulmonary delivery. The naringenin-HPβCD inclusion was characterized by phase solubility study, XRD, differential scanning calorimetry (DSC), proton nuclear magnetic resonance (1HNMR), and two-dimensional rotating frame Overhauser effect spectroscopy (2D ROESY). The in vitro permeability of the inclusion was evaluated on Calu-3 cells and the pharmacokinetic profile of pulmonary delivery was investigated in Sprague-Dawley (SD) rats. Based on the linear model of phase solubility study, the relationship between naringenin and HPβCD was identified as AL type with a 1:1 stoichiometry. XRD, DSC, and NMR studies indicated that the entire naringenin molecule is encapsulated into the cavity of HPβCD. HPβCD could increase the concentration of naringenin in the epithelium-lining fluid (ELF) of Calu-3 cells and act as a sustained release system for naringenin. The pharmacokinetic profile of naringenin-HPβCD inclusion showed rapid response and higher local concentration by pulmonary delivery. In conclusion, pulmonary delivery of naringenin-HPβCD inclusion is a promising formulation strategy, which could provide a new possibility for the clinical application of naringenin