MoGA: Searching Beyond MobileNetV3

The evolution of MobileNets has laid a solid foundation for neural network applications on mobile end. With the latest MobileNetV3, neural architecture search again claimed its supremacy in network design. Unfortunately, till today all mobile methods mainly focus on CPU latencies instead of GPU, the latter, however, is much preferred in practice for it has faster speed, lower overhead and less interference. Bearing the target hardware in mind, we propose the first Mobile GPU-Aware (MoGA) neural architecture search in order to be precisely tailored for real-world applications. Further, the ultimate objective to devise a mobile network lies in achieving better performance by maximizing the utilization of bounded resources. Urging higher capability while restraining time consumption is not reconcilable. We alleviate the tension by weighted evolution techniques. Moreover, we encourage increasing the number of parameters for higher representational power. With 200x fewer GPU days than MnasNet, we obtain a series of models that outperform MobileNetV3 under the similar latency constraints, i.e., MoGA-A achieves 75.9% top-1 accuracy on ImageNet, MoGA-B meets 75.5% which costs only 0.5 ms more on mobile GPU. MoGA-C best attests GPU-awareness by reaching 75.3% and being slower on CPU but faster on GPU.The models and test code is made available here https://github.com/xiaomi-automl/MoGA.Comment: Accepted by ICASSP202

Application of a Dense Fusion Attention Network in Fault Diagnosis of Centrifugal Fan

Although the deep learning recognition model has been widely used in the condition monitoring of rotating machinery. However, it is still a challenge to understand the correspondence between the structure and function of the model and the diagnosis process. Therefore, this paper discusses embedding distributed attention modules into dense connections instead of traditional dense cascading operations. It not only decouples the influence of space and channel on fault feature adaptive recalibration feature weights, but also forms a fusion attention function. The proposed dense fusion focuses on the visualization of the network diagnosis process, which increases the interpretability of model diagnosis. How to continuously and effectively integrate different functions to enhance the ability to extract fault features and the ability to resist noise is answered. Centrifugal fan fault data is used to verify this network. Experimental results show that the network has stronger diagnostic performance than other advanced fault diagnostic models

On the Capacity Losses Seen for Optimized Nano‐Si Composite Electrodes in Li‐Metal Half‐Cells

While the use of silicon‐based electrodes can increase the capacity of Li‐ion batteries considerably, their application is associated with significant capacity losses. In this work, the influences of solid electrolyte interphase (SEI) formation, volume expansion, and lithium trapping are evaluated for two different electrochemical cycling schemes using lithium‐metal half‐cells containing silicon nanoparticle–based composite electrodes. Lithium trapping, caused by incomplete delithiation, is demonstrated to be the main reason for the capacity loss while SEI formation and dissolution affect the accumulated capacity loss due to a decreased coulombic efficiency. The capacity losses can be explained by the increasing lithium concentration in the electrode causing a decreasing lithiation potential and the lithiation cut‐off limit being reached faster. A lithium‐to‐silicon atomic ratio of 3.28 is found for a silicon electrode after 650 cycles using 1200 mAhg−1 capacity limited cycling. The results further show that the lithiation step is the capacity‐limiting step and that the capacity losses can be minimized by increasing the efficiency of the delithiation step via the inclusion of constant voltage delithiation steps. Lithium trapping due to incomplete delithiation consequently constitutes a very important capacity loss phenomenon for silicon composite electrodes