High-performance acceleration of 2-D and 3D CNNs on FPGAs using static block floating point

Over the past few years, 2-D convolutional neural networks (CNNs) have demonstrated their great success in a wide range of 2-D computer vision applications, such as image classification and object detection. At the same time, 3-D CNNs, as a variant of 2-D CNNs, have shown their excellent ability to analyze 3-D data, such as video and geometric data. However, the heavy algorithmic complexity of 2-D and 3-D CNNs imposes a substantial overhead over the speed of these networks, which limits their deployment in real-life applications. Although various domain-specific accelerators have been proposed to address this challenge, most of them only focus on accelerating 2-D CNNs, without considering their computational efficiency on 3-D CNNs. In this article, we propose a unified hardware architecture to accelerate both 2-D and 3-D CNNs with high hardware efficiency. Our experiments demonstrate that the proposed accelerator can achieve up to 92.4% and 85.2% multiply-accumulate efficiency on 2-D and 3-D CNNs, respectively. To improve the hardware performance, we propose a hardware-friendly quantization approach called static block floating point (BFP), which eliminates the frequent representation conversions required in traditional dynamic BFP arithmetic. Comparing with the integer linear quantization using zero-point, the static BFP quantization can decrease the logic resource consumption of the convolutional kernel design by nearly 50% on a field-programmable gate array (FPGA). Without time-consuming retraining, the proposed static BFP quantization is able to quantize the precision to 8-bit mantissa with negligible accuracy loss. As different CNNs on our reconfigurable system require different hardware and software parameters to achieve optimal hardware performance and accuracy, we also propose an automatic tool for parameter optimization. Based on our hardware design and optimization, we demonstrate that the proposed accelerator can achieve 3.8-5.6 times higher energy efficiency than graphics processing unit (GPU) implementation. Comparing with the state-of-the-art FPGA-based accelerators, our design achieves higher generality and up to 1.4-2.2 times higher resource efficiency on both 2-D and 3-D CNNs

Two novel transcriptional regulators are essential for infection-related morphogenesis and pathogenicity of the rice blast fungus Magnaporthe oryzae.

This is the final version of the article. Available from the publisher via the DOI in this record.The cyclic AMP-dependent protein kinase A signaling pathway plays a major role in regulating plant infection by the rice blast fungus Magnaporthe oryzae. Here, we report the identification of two novel genes, MoSOM1 and MoCDTF1, which were discovered in an insertional mutagenesis screen for non-pathogenic mutants of M. oryzae. MoSOM1 or MoCDTF1 are both necessary for development of spores and appressoria by M. oryzae and play roles in cell wall differentiation, regulating melanin pigmentation and cell surface hydrophobicity during spore formation. MoSom1 strongly interacts with MoStu1 (Mstu1), an APSES transcription factor protein, and with MoCdtf1, while also interacting more weakly with the catalytic subunit of protein kinase A (CpkA) in yeast two hybrid assays. Furthermore, the expression levels of MoSOM1 and MoCDTF1 were significantly reduced in both Δmac1 and ΔcpkA mutants, consistent with regulation by the cAMP/PKA signaling pathway. MoSom1-GFP and MoCdtf1-GFP fusion proteins localized to the nucleus of fungal cells. Site-directed mutagenesis confirmed that nuclear localization signal sequences in MoSom1 and MoCdtf1 are essential for their sub-cellular localization and biological functions. Transcriptional profiling revealed major changes in gene expression associated with loss of MoSOM1 during infection-related development. We conclude that MoSom1 and MoCdtf1 functions downstream of the cAMP/PKA signaling pathway and are novel transcriptional regulators associated with cellular differentiation during plant infection by the rice blast fungus.Funding: This work was supported by National Key Basic Research and Development Program of China (2012CB114002), by Program for Changjiang Scholars and Innovative Research Team in University (IRT0943), by the Natural Science Foundation of China (Grant Nos. 30970129 and 31071648) and the Doctoral Fund of Ministry of Education of China (20100101110097) to ZW

Effect of different drying methods on the protein and product quality of hairtail fish meat gel

Three different methods, namely hot air drying (HA), microwave vacuum drying (MV), and vacuum freeze drying (FD), were employed to investigate the effect of drying method on the quality of hairtail fish meat gel. Compared with HA and MV, FD samples showed a better quality in terms of moisture content, water absorption index, and water solubility index, and had the highest overall acceptance in sensory evaluation. FD preserved the protein from degradation and formed an ordered porous microstructure. The nitrogen fraction assay revealed that protein was degraded into 40–100 kDa fragments during drying in HA, which was almost not affected by MV and FD. Overall, FD was the most suitable method for drying of meat gel made from hairtail, followed by MV and HA

F-E3D: FPGA-based acceleration of an efficient 3D convolutional neural network for human action recognition

Three-dimensional convolutional neural networks (3D CNNs) have demonstrated their outstanding classification accuracy for human action recognition (HAR). However, the large number of computations and parameters in 3D CNNs limits their deployability in real-life applications. To address this challenge, this paper adopts an algorithm-hardware co-design method by proposing an efficient 3D CNN building unit called 3D-1 bottleneck residual block (3D-1 BRB) at the algorithm level, and a corresponding FPGA-based hardware architecture called F-E3D at hardware level. Based on 3D-1 BRB, a novel 3D CNN model called E3DNet is developed, which achieves nearly 37 times reduction in model size and 5% improvement in accuracy compared to standard 3D CNNs on the UCF101 dataset. Together with several hardware optimizations, including 3D fused BRB, online blocking and kernel reuse, the proposed F-E3D is nearly 13 times faster than a previous FPGA design for 3D CNNs, with performance and accuracy comparable to other state-of-the-art 3D CNN models on GPU platforms while requiring only 7% of their energy consumption

Enhanced heterogeneous nucleation of Al6(Fe,Mn) compound in Al alloys by interfacial segregation of Mn on TiB2 particles surface

Copyright © 2022 The Author(s). In this study, the composition templating theory for heterogeneous nucleation was applied to achieve TiB2 particles with Mn segregation on the surface, which supplied the initial composition for the heterogeneous nucleation. The interfacial segregation of added alloy element Mn and the other common impurities, such as Fe and Si, was investigated with scanning transmission electron microscopy (STEM). The modified TiB2 particles was applied in Al-2.0Mn-1.0Fe alloys to test its effects on grain refinement of Al6(Fe,Mn) compound. The interfaces between Al6(Fe,Mn) particles and the engulfed TiB2 particles were examined with TEM observation.Engineering and Physical Sciences Research Council (EPSRC) Grant EP/H026177/1

Mechanism for Si Poisoning of Al-Ti-B Grain Refiners in Al Alloys

EPSRC (grant number EP/N007638 /1)

Composition templating for heterogeneous nucleation of intermetallic compounds

Data availability: The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. All data generated or analysed during this study are included in this published article.Refinement of intermetallic compounds (IMCs) through enhancing heterogeneous nucleation during casting process is an important approach to improve the properties of aluminium alloys, which greatly increases the economy value of recycled Al-alloys. However, heterogeneous nucleation of IMCs is inherently more difficult than that of a pure metal or a solid solution. It requires not only creation of a crystal structure but also the positioning of 2 or more different types of atoms in the lattice with specific composition close to that of the nucleated IMCs. Previous understanding on heterogeneous nucleation is based on structural templating, usually considering the small lattice misfit at the interface between the nucleating solid and substrate. In this work, we proposed a hypothesis and demonstrated that composition templating plays a critical role in heterogeneous nucleation of IMCs. The experimental results revealed that segregation of Fe atoms on the AlB2 surface, i.e., the Fe modified AlB2 particle, provides the required composition templating and hence enhances heterogeneous nucleation of α-Al15(Fe, Mn)3Si2, resulting in a significant refinement of the α-Al15(Fe, Mn)3Si2 particles in an Al-5 Mg-2Si-1.0Mn-1.2Fe alloy.Engineering and Physical Sciences Research Council (EPSRC) (UK) for under grant number EP/N007638/1 (Future Liquid Metal Engineering Hub)

Computational prediction of inter-species relationships through omics data analysis and machine learning.

Antibiotic resistance and its rapid dissemination around the world threaten the efficacy of currently-used medical treatments and call for novel, innovative approaches to manage multi-drug resistant infections. Phage therapy, i.e., the use of viruses (phages) to specifically infect and kill bacteria during their life cycle, is one of the most promising alternatives to antibiotics. It is based on the correct matching between a target pathogenic bacteria and the therapeutic phage. Nevertheless, correctly matching them is a major challenge. Currently, there is no systematic method to efficiently predict whether phage-bacterium interactions exist and these pairs must be empirically tested in laboratory. Herein, we present our approach for developing a computational model able to predict whether a given phage-bacterium pair can interact based on their genome. Based on public data from GenBank and phagesDB.org, we collected more than a thousand positive phage-bacterium interactions with their complete genomes. In addition, we generated putative negative (i.e., non-interacting) pairs. We extracted, from the collected genomes, a set of informative features based on the distribution of predictive protein-protein interactions and on their primary structure (e.g. amino-acid frequency, molecular weight and chemical composition of each protein). With these features, we generated multiple candidate datasets to train our algorithms. On this base, we built predictive models exhibiting predictive performance of around 90% in terms of F1-score, sensitivity, specificity, and accuracy, obtained on the test set with 10-fold cross-validation. These promising results reinforce the hypothesis that machine learning techniques may produce highly-predictive models accelerating the search of interacting phage-bacteria pairs

Reed Parrotbill nest predation by tidal mudflat crabs: Evidence for an ecological trap?

Understanding the relationships between nesting habitat quality and predation risk is essential for developing appropriate conservation management for threatened species. This is particularly relevant where anthropogenic pressures could decouple the environmental cues used by birds to assess nesting habitat quality from increased predation risk. In this study, we conducted a series of surveys and nest experiments to examine the nest predation rates of Reed Parrotbill (Paradoxornis heudei ) a passerine bird between inland and tidal reed-bed wetland habitats, at Yellow River Delta National Nature Reserve, Eastern China during 2008, and 2010–2012. We found significant differences in the habitat structural characteristics between the two adjacent wetland habitats that are critical for Reed Parrotbill nest-site selection. Experimental trials using recently constructed and abandoned Reed Parrotbill nests as ‘artificial nests, quail eggs and predator-exclusion measures, revealed that tidal mudflat crab (Helice tientsinensis) was the primary cause of Reed Parrotbill egg predation in tidal reed-bed habitat. Annual predation rates of real nests from inland reed-bed habitat varied from 35% to 68%, and predation rates of artificial nests were much lower than those from real nests. Pitfall sampling revealed that the abundance of tidal mudflat crabs was significantly higher in tidal reed-bed habitat. Our data suggested that Reed Parrotbills breeding in tidal reed-bed habitats may be highly vulnerable due to extremely high rates of nest predation (up to 100%), caused primarily by the high density of tidal mudflat crabs. The incongruence between nest-site habitat preference and nest survival indicated an ecological trap scenario, which requires further studies on its proximate and ultimate causes as well as the development of effective conservation management for the Reed Parrotbill