Towards Ultra-High Performance and Energy Efficiency of Deep Learning Systems: An Algorithm-Hardware Co-Optimization Framework

Hardware accelerations of deep learning systems have been extensively investigated in industry and academia. The aim of this paper is to achieve ultra-high energy efficiency and performance for hardware implementations of deep neural networks (DNNs). An algorithm-hardware co-optimization framework is developed, which is applicable to different DNN types, sizes, and application scenarios. The algorithm part adopts the general block-circulant matrices to achieve a fine-grained tradeoff between accuracy and compression ratio. It applies to both fully-connected and convolutional layers and contains a mathematically rigorous proof of the effectiveness of the method. The proposed algorithm reduces computational complexity per layer from O($n^2$) to O($n\log n$) and storage complexity from O($n^2$) to O($n$), both for training and inference. The hardware part consists of highly efficient Field Programmable Gate Array (FPGA)-based implementations using effective reconfiguration, batch processing, deep pipelining, resource re-using, and hierarchical control. Experimental results demonstrate that the proposed framework achieves at least 152X speedup and 71X energy efficiency gain compared with IBM TrueNorth processor under the same test accuracy. It achieves at least 31X energy efficiency gain compared with the reference FPGA-based work.Comment: 6 figures, AAAI Conference on Artificial Intelligence, 201

Comparative phylogenomics and multi-gene cluster analyses of the Citrus Huanglongbing (HLB)-associated bacterium Candidatus Liberibacter

<p>Abstract</p> <p>Background</p> <p>Huanglongbing (HLB, previously known as citrus greening), is associated with <it>Candidatus </it>Liberibacter species and is a serious threat to citrus production world-wide. The pathogen is a Gram negative, unculturable, phloem-limited bacterium with limited known genomic information. Expanding the genetic knowledge of this organism may provide better understanding of the pathogen and possibly develop effective strategies for control and management of HLB.</p> <p>Results</p> <p>Here, we report cloning and characterization of an additional 14.7 Kb of new genomic sequences from three different genomic regions of the <it>Candidatus </it>Liberibacter asiaticus (Las). Sequence variation analyses among the available <it>Ca</it>. Liberibacter species sequences as well as the newly cloned 1.5 Kb of <it>rpo</it>B gene from different <it>Ca</it>. Liberibacter strains have identified INDELs and SNPs. Phylogenetic analysis of the deduced protein sequences from the cloned regions characterizes the HLB-associated <it>Candidatus </it>Liberibacter as a new clade in the sub-division of the α-proteobacteria.</p> <p>Conclusion</p> <p>Comparative analyses of the cloned gene regions of <it>Candidatus </it>Liberibacter with members of the order Rhizobiales suggest overall gene structure and order conservation, albeit with minor variations including gene decay due to the identified pseudogenes. The newly cloned gene regions contribute to our understanding of the molecular aspects of genomic evolution of <it>Ca</it>. Liberibacter.</p

Intermediate-mass black holes: finding of episodic, large-scale, and powerful jet activity in a dwarf galaxy

Dwarf galaxies are characterized by a very low luminosity and low mass. Because of significant accretion and ejection activity of massive black holes, some dwarf galaxies also host lo w-luminosity acti ve galactic nuclei (AGNs). In a few dwarf AGNs, very long baseline interferometry (VLBI) observations have found faint non-thermal radio emission. SDSS J090613.77 + 561015.2 is a dwarf AGN owning an intermediate-mass black hole (IMBH) with a mass of M-BH = 3 . 6 (+5.9) (-2.3 )x 10 (5) M(? )and showing a rarely seen two-component radio structure in its radio nucleus. To further probe their nature, i.e. the IMBH jet activity, we performed additional deep observations with the European VLBI Network (EVN) at 1.66 and 4.99 GHz. We find the more diffuse emission regions and structure details. These new EVN imaging results allow us to reveal a two-sided jet morphology with a size up to about 150 mas (projected length similar to 140 pc) and a radio luminosity of about 3 x 10( 38) erg s( -1). The peak feature has an optically thin radio spectrum and thus more likely represents a relatively young ejecta instead of a jet base. The EVN study on SDSS J090613.77 + 561015.2 demonstrates the existence of episodic, relatively large-scale, and powerful IMBH jet activity in dwarf AGNs. Moreo v er, we collected a small sample of VLBI-detected dwarf AGNs and investigated their connections with normal AGNs. We notice that these radio sources in the dwarf AGNs tend to have steep spectra and small linear sizes, and possibly represent ejecta from scaled-down episodic jet activity

Epiphytic microbiota source stimulates the fermentation profile and bacterial community of alfalfa-corn mixed silage

The epiphytic microbiota source on plants plays a crucial role in the production of high-quality silage. To gain a better understanding of its contribution, the microbiota of alfalfa (M1C0), corn (M0C1) and the resulting mixture (M1C1) was applied in alfalfa-corn mixed silage production system. M1C0 decreased ammonia-N levels in terms of total nitrogen (57.59–118.23 g/kg TN) and pH (3.59–4.40) values (p &lt; 0.01), which increased lactic acid (33.73–61.89 g/kg DM) content (p &lt; 0.01). Consequently, this resulted in higher residual water-soluble carbohydrate (29.13–41.76 g/kg DM) and crude protein (152.54–167.91 g/kg DM) contents, as well as lower NDF (427.27 g/kg DM) and ADF (269.53 g/kg DM) contents in the silage compared to M1C1- and M0C1-treated samples. Moreover, M1C0 silage showed significantly higher bacterial alpha diversity indices (p &lt; 0.05), including the number of observed species and Chao1 and Shannon diversity indices, at the later stages of ensiling. Lactobacillus, Kosakonia and Enterobacter were the dominant bacterial species in silages, with a relative abundance of &gt;80%. However, the abundance of Lactobacillus amylovorus in M0C1- and M1C1-treated silage increased (p &lt; 0.01) in the late stages of ensiling. These findings confirmed that the epiphytic microbiota source exerts competitive effects during anaerobic storage of alfalfa-corn mixed silage

Effective electro-optical modulation with high extinction ratio by a graphene-silicon microring resonator

Graphene opens up for novel optoelectronic applications thanks to its high carrier mobility, ultra-large absorption bandwidth, and extremely fast material response. In particular, the opportunity to control optoelectronic properties through tuning of Fermi level enables electro-optical modulation, optical-optical switching, and other optoelectronics applications. However, achieving a high modulation depth remains a challenge because of the modest graphene-light interaction in the graphene-silicon devices, typically, utilizing only a monolayer or few layers of graphene. Here, we comprehensively study the interaction between graphene and a microring resonator, and its influence on the optical modulation depth. We demonstrate graphene-silicon microring devices showing a high modulation depth of 12.5 dB with a relatively low bias voltage of 8.8 V. On-off electro-optical switching with an extinction ratio of 3.8 dB is successfully demonstrated by applying a square-waveform with a 4 V peak-to-peak voltage.Comment: 12 pages, including 7 figure

Pan-cancer analysis reveals that G6PD is a prognostic biomarker and therapeutic target for a variety of cancers

BackgroundDespite accumulating evidence revealing that Glucose-6-phosphate dehydrogenase (G6PD) is highly expressed in many tumor tissues and plays a remarkable role in cancer tumorigenesis and progression, there is still a lack of G6PD pan-cancer analysis. This study was designed to analyze the expression status and prognostic significance of G6PD in pan-cancer.MethodsG6PD expression data were obtained from multiple data resources including the Genotype-Tissue Expression, the Cancer Genome Atlas, and the Tumor Immunity Estimation Resource. These data were used to assess the G6PD expression, prognostic value, and clinical characteristics. The ESTIMATE algorithms were used to analyze the association between G6PD expression and immune-infiltrating cells and the tumor microenvironment. The functional enrichment analysis was also performed across pan-cancer. In addition, the GDSC1 database containing 403 drugs was utilized to explore the relationship between drug sensitivity and G6PD expression levels. Furthermore, we also performed clinical validation and in vitro experiments to further validate the role of G6PD in hepatocellular carcinoma (HCC) cells and its correlation with prognosis. The R software was used for statistical analysis and data visualization.ResultsG6PD expression was upregulated in most cancers compared to their normal counterparts. The study also revealed that G6PD expression was a prognostic indicator and high levels of G6PD expression were correlated with worse clinical prognosis including overall survival, disease-specific survival, and progression-free interval in multiple cancers. Furthermore, the G6PD level was also related to cancer immunity infiltration in most of the cancers, especially in KIRC, LGG, and LIHC. In addition to this, G6PD expression was positively related to pathological stages of KIRP, BRCA, KIRC, and LIHC. Functional analysis and protein-protein interactions network results revealed that G6PD was involved in metabolism-related activities, immune responses, proliferation, and apoptosis. Drug sensitivity analysis showed that IC50 values of most identified anti-cancer drugs were positively correlated with the G6PD expression. Notably, in vitro functional validation showed that G6PD knockdown attenuated the phenotypes of proliferation in HCC.ConclusionG6PD may serve as a potential prognostic biomarker for cancers and may be a potential therapeutic target gene for tumor therapy