Full Stack Optimization of Transformer Inference: a Survey

Recent advances in state-of-the-art DNN architecture design have been moving toward Transformer models. These models achieve superior accuracy across a wide range of applications. This trend has been consistent over the past several years since Transformer models were originally introduced. However, the amount of compute and bandwidth required for inference of recent Transformer models is growing at a significant rate, and this has made their deployment in latency-sensitive applications challenging. As such, there has been an increased focus on making Transformer models more efficient, with methods that range from changing the architecture design, all the way to developing dedicated domain-specific accelerators. In this work, we survey different approaches for efficient Transformer inference, including: (i) analysis and profiling of the bottlenecks in existing Transformer architectures and their similarities and differences with previous convolutional models; (ii) implications of Transformer architecture on hardware, including the impact of non-linear operations such as Layer Normalization, Softmax, and GELU, as well as linear operations, on hardware design; (iii) approaches for optimizing a fixed Transformer architecture; (iv) challenges in finding the right mapping and scheduling of operations for Transformer models; and (v) approaches for optimizing Transformer models by adapting the architecture using neural architecture search. Finally, we perform a case study by applying the surveyed optimizations on Gemmini, the open-source, full-stack DNN accelerator generator, and we show how each of these approaches can yield improvements, compared to previous benchmark results on Gemmini. Among other things, we find that a full-stack co-design approach with the aforementioned methods can result in up to 88.7x speedup with a minimal performance degradation for Transformer inference

Advances in bioorganic molecules inspired degradation and surface modifications on Mg and its alloys

Mg alloys possess biodegradability, suitable mechanical properties, and biocompatibility, which make them possible to be used as biodegradable implants. However, the uncontrollable degradation of Mg alloys limits their general applications. In addition to the factors from the metallic materials themselves, like alloy compositions, heat treatment process and microstructure, some external factors, relating to the test/service environment, also affect the degradation rate of Mg alloys, such as inorganic salts, bioorganic small molecules, bioorganic macromolecules. The influence of bioorganic molecules on Mg corrosion and its protection has attracted more and more attentions. In this work, the cutting-edge advances in the influence of bioorganic molecules (i.e., protein, glucose, amino acids, vitamins and polypeptide) and their coupling effect on Mg degradation and the formation of protection coatings were reviewed. The research orientations of biomedical Mg alloys in exploring degradation mechanisms in vitro were proposed, and the impact of bioorganic molecules on the protective approaches were also explored

A comprehensive numerical model of steady state saltation (COMSALT)

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/95613/1/jgrd15469.pd

Structural insights into the electron/proton transfer pathways in the quinol : fumarate reductase from Desulfovibrio gigas

Guan, H., Hsieh, Y., Lin, P. et al. Structural insights into the electron/proton transfer pathways in the quinol : fumarate reductase from Desulfovibrio gigas. Sci Rep 8, 14935 (2018) doi:10.1038/s41598-018-33193-

Structural insights into the electron/proton transfer pathways in the quinol:fumarate reductase from Desulfovibrio gigas

The membrane-embedded quinol:fumarate reductase (QFR) in anaerobic bacteria catalyzes the reduction of fumarate to succinate by quinol in the anaerobic respiratory chain. The electron/proton-transfer pathways in QFRs remain controversial. Here we report the crystal structure of QFR from the anaerobic sulphate-reducing bacterium Desulfovibrio gigas (D. gigas) at 3.6 Å resolution. The structure of the D. gigas QFR is a homo-dimer, each protomer comprising two hydrophilic subunits, A and B, and one transmembrane subunit C, together with six redox cofactors including two b-hemes. One menaquinone molecule is bound near heme b_L in the hydrophobic subunit C. This location of the menaquinone-binding site differs from the menaquinol-binding cavity proposed previously for QFR from Wolinella succinogenes. The observed bound menaquinone might serve as an additional redox cofactor to mediate the proton-coupled electron transport across the membrane. Armed with these structural insights, we propose electron/proton-transfer pathways in the quinol reduction of fumarate to succinate in the D. gigas QFR

Association between residential greenness and metabolic syndrome in Chinese adults

Background: Residing in greener areas has several health benefits, but no study to date has examined the effects of greenness on metabolic syndrome (MetS). We aimed to assess associations between residential greenness and MetS prevalence in China, and to explore whether air pollution and physical activity mediated any observed associations. Methods: We analyzed data from 15,477 adults who participated in the 33 Communities Chinese Health Study during 2009. We defined MetS according to standard guidelines for Chinese populations. Residential greenness was estimated using the Normalized Difference Vegetation Index (NDVI), the Soil Adjusted Vegetation Index (SAVI), and the Vegetation Continuous Field (VCF). We used generalized linear mixed models to assess the associations between greenness and MetS, and mediation analyses to explore potential mechanisms underlying the associations. Results: Higher greenness levels were associated with lower odds of MetS [e.g., for every interquartile range increase of NDVI500-m, SAVI(500-m), and VCF500-m the adjusted odds ratio of MetS was 0.81 (95% confidence interval: 0.70-0.93), 0.80 (95% confidence interval: 0.69-0.93), and 0.91 (95% confidence interval: 0.83-1.00), respectively]. The direction and the magnitude of the associations persisted in several sensitivity analyses. Stratified analyses showed that age and household income modified the associations, with greater effect estimates observed in participants younger than 65 years old or those with higher household income. Particulate matter with an aerodynamic diameter <= 10 mu m nitrogen dioxide, and ozone mediated 2.1-20.3% of the associations between greenness and MetS;no evidence of mediation was observed for physical activity. Conclusions: Our findings suggest a beneficial association for residential greenness and MetS in Chinese urban dwellers, especially for participants younger than 65 years old and those with higher household income. Particulate matter with an aerodynamic diameter <= 10 mu m nitrogen dioxide and ozone, but not physical activity, may only partially mediate the association