FastDeepIoT: Towards Understanding and Optimizing Neural Network Execution Time on Mobile and Embedded Devices

Deep neural networks show great potential as solutions to many sensing application problems, but their excessive resource demand slows down execution time, pausing a serious impediment to deployment on low-end devices. To address this challenge, recent literature focused on compressing neural network size to improve performance. We show that changing neural network size does not proportionally affect performance attributes of interest, such as execution time. Rather, extreme run-time nonlinearities exist over the network configuration space. Hence, we propose a novel framework, called FastDeepIoT, that uncovers the non-linear relation between neural network structure and execution time, then exploits that understanding to find network configurations that significantly improve the trade-off between execution time and accuracy on mobile and embedded devices. FastDeepIoT makes two key contributions. First, FastDeepIoT automatically learns an accurate and highly interpretable execution time model for deep neural networks on the target device. This is done without prior knowledge of either the hardware specifications or the detailed implementation of the used deep learning library. Second, FastDeepIoT informs a compression algorithm how to minimize execution time on the profiled device without impacting accuracy. We evaluate FastDeepIoT using three different sensing-related tasks on two mobile devices: Nexus 5 and Galaxy Nexus. FastDeepIoT further reduces the neural network execution time by $48\%$ to $78\%$ and energy consumption by $37\%$ to $69\%$ compared with the state-of-the-art compression algorithms.Comment: Accepted by SenSys '1

STFNets: Learning Sensing Signals from the Time-Frequency Perspective with Short-Time Fourier Neural Networks

Recent advances in deep learning motivate the use of deep neural networks in Internet-of-Things (IoT) applications. These networks are modelled after signal processing in the human brain, thereby leading to significant advantages at perceptual tasks such as vision and speech recognition. IoT applications, however, often measure physical phenomena, where the underlying physics (such as inertia, wireless signal propagation, or the natural frequency of oscillation) are fundamentally a function of signal frequencies, offering better features in the frequency domain. This observation leads to a fundamental question: For IoT applications, can one develop a new brand of neural network structures that synthesize features inspired not only by the biology of human perception but also by the fundamental nature of physics? Hence, in this paper, instead of using conventional building blocks (e.g., convolutional and recurrent layers), we propose a new foundational neural network building block, the Short-Time Fourier Neural Network (STFNet). It integrates a widely-used time-frequency analysis method, the Short-Time Fourier Transform, into data processing to learn features directly in the frequency domain, where the physics of underlying phenomena leave better foot-prints. STFNets bring additional flexibility to time-frequency analysis by offering novel nonlinear learnable operations that are spectral-compatible. Moreover, STFNets show that transforming signals to a domain that is more connected to the underlying physics greatly simplifies the learning process. We demonstrate the effectiveness of STFNets with extensive experiments. STFNets significantly outperform the state-of-the-art deep learning models in all experiments. A STFNet, therefore, demonstrates superior capability as the fundamental building block of deep neural networks for IoT applications for various sensor inputs

Two dimensional metal halide perovskites: Promising candidates for light-emitting diodes

Two dimensional halide perovskites are emerging as attractive electroluminescent materials for developing high-performance light-emitting devices owing to their unique structures and/or superior optoelectronic properties. This review begins with an introduction to the working principles of and the key figures for evaluating the performance of LEDs. Secondly, the structure and opto-electronic properties of two dimensional perovskites are summarized and discussed. Their advantages in LED application over their 3D counterparts are systematically analyzed. Following the theoretically discussion, the progresses on the preparation of two dimensional perovskite materials as well as their performances in LEDs have been summarized. At last, several challenges and prospects are presented for achieving high performance 2D perovskite-based LEDs. (C) 2018 Science Press and Dalian Institute of Chemical Physics, Chinese Academy of Sciences. Published by Elsevier B.V. and Science Press. All rights reserved

Efficient perovskite solar cells via surface passivation by a multifunctional small organic ionic compound

Surface passivation is a proven strategy for preparing high efficiency planar perovskite solar cells. Herein, we report an effective surface passivation strategy using the multifunctional small organic ionic compound 1-ethylpyridinium chloride (EPC) in combination with (FAPbI(3))(0.95)(MAPbBr(3))(0.05). It is found that the nitrogen atom in the pyridine group forms chemical bonds with under-coordinated lead ions in the perovskite film whereby the defect density is significantly reduced. The organic groups, including ethyl and pyridine, furthermore provide higher hydrophobicity for improved moisture stability. Finally, the chlorine anions were also found to play an important role in the defect passivation while further improving the perovskite crystallinity. Detailed theoretical study confirms that the EPC is indeed a good passivation agent for multiple defects. As a result, the power conversion efficiency is increased from 19.52% to as high as 21.19% via the EPC passivation and the devices show reduced hysteresis and increased stability

Compositional Control in 2D Perovskites with Alternating Cations in the Interlayer Space for Photovoltaics with Efficiency over 18%

2D perovskites stabilized by alternating cations in the interlayer space (ACI) represent a very new entry as highly efficient semiconductors for solar cells approaching 15% power conversion efficiency (PCE). However, further improvements will require understanding of the nature of the films, e.g., the thickness distribution and charge-transfer characteristics of ACI quantum wells (QWs), which are currently unknown. Here, efficient control of the film quality of ACI 2D perovskite (GA)(MA)(n)PbnI3n+1 (n = 3) QWs via incorporation of methylammonium chloride as an additive is demonstrated. The morphological and optoelectronic characterizations unambiguously demonstrate that the additive enables a larger grain size, a smoother surface, and a gradient distribution of QW thickness, which lead to enhanced photocurrent transport/extraction through efficient charge transfer between low-n and high-n QWs and suppressed nonradiative charge recombination. Therefore, the additive-treated ACI perovskite film delivers a champion PCE of 18.48%, far higher than the pristine one (15.79%) due to significant improvements in open-circuit voltage and fill factor. This PCE also stands as the highest value for all reported 2D perovskite solar cells based on the ACI, Ruddlesden-Popper, and Dion-Jacobson families. These findings establish the fundamental guidelines for the compositional control of 2D perovskites for efficient photovoltaics