Service Abstractions for Scalable Deep Learning Inference at the Edge

Deep learning driven intelligent edge has already become a reality, where millions of mobile, wearable, and IoT devices analyze real-time data and transform those into actionable insights on-device. Typical approaches for optimizing deep learning inference mostly focus on accelerating the execution of individual inference tasks, without considering the contextual correlation unique to edge environments and the statistical nature of learning-based computation. Specifically, they treat inference workloads as individual black boxes and apply canonical system optimization techniques, developed over the last few decades, to handle them as yet another type of computation-intensive applications. As a result, deep learning inference on edge devices still face the ever increasing challenges of customization to edge device heterogeneity, fuzzy computation redundancy between inference tasks, and end-to-end deployment at scale. In this thesis, we propose the first framework that automates and scales the end-to-end process of deploying efficient deep learning inference from the cloud to heterogeneous edge devices. The framework consists of a series of service abstractions that handle DNN model tailoring, model indexing and query, and computation reuse for runtime inference respectively. Together, these services bridge the gap between deep learning training and inference, eliminate computation redundancy during inference execution, and further lower the barrier for deep learning algorithm and system co-optimization. To build efficient and scalable services, we take a unique algorithmic approach of harnessing the semantic correlation between the learning-based computation. Rather than viewing individual tasks as isolated black boxes, we optimize them collectively in a white box approach, proposing primitives to formulate the semantics of the deep learning workloads, algorithms to assess their hidden correlation (in terms of the input data, the neural network models, and the deployment trials) and merge common processing steps to minimize redundancy

Increased Functional Brain Network Efficiency During Audiovisual Temporal Asynchrony Integration Task in Aging

Audiovisual integration significantly changes over the lifespan, but age-related functional connectivity in audiovisual temporal asynchrony integration tasks remains underexplored. In the present study, electroencephalograms (EEGs) of 27 young adults (22–25 years) and 25 old adults (61–76 years) were recorded during an audiovisual temporal asynchrony integration task with seven conditions [auditory (A), visual (V), AV, A50V, A100V, V50A and V100A]. We calculated the phase lag index (PLI)-weighted connectivity networks modulated by the audiovisual tasks and found that the PLI connections showed obvious dynamic changes after stimulus onset. In the theta (4–7 Hz) and alpha (8–13 Hz) bands, the AV and V50A conditions induced stronger functional connections and higher global and local efficiencies, reflecting a stronger audiovisual integration effect, which was attributed to the auditory information arriving at the primary auditory cortex earlier than the visual information reaching the primary visual cortex. Importantly, the functional connectivity and network efficiencies of old adults revealed higher global and local efficiencies and higher degree in both the theta and alpha bands. These larger network efficiencies indicated that old adults might experience more difficulties in attention and cognitive control during the audiovisual integration task with temporal asynchrony than young adults. There were significant associations between network efficiencies and peak time of integration only in young adults. We propose that an audiovisual task with multiple conditions might arouse the appropriate attention in young adults but would lead to a ceiling effect in old adults. Our findings provide new insights into the network topography of old adults during audiovisual integration and highlight higher functional connectivity and network efficiencies due to greater cognitive demand

Research on the Range-Frequency Interference Characteristics of Target Scattering Field in a Shallow Water Waveguide

Based on the target scattering model and the normal mode theory in a shallow water waveguide, a mathematical model of the acoustic intensity under the coupling condition of the target and the environment was deduced, and the interference striations in the monostatic and bistatic configuration were obtained by simulations. Further, a field experiment was carried out in a lake, and the data were collected for a spherical target in two frequency bands, i.e., 20−40 kHz and 40−80 kHz. The experiment results showed good agreement with the simulations. The results of the simulation and the experiment showed that the existence of the target made the interference phenomenon in shallow water waveguides more complex, and the range-frequency interference characteristics were closely related to the configuration of the sonar system, the target scattering function, the frequency range, and the target movement trajectory. These interference phenomena were found and theoretically analyzed in the paper. The research results can be applied to target detection and recognition, signal parameter estimation, target tracking, and other fields

Evaluation of Multiple Satellite Precipitation Products and Their Use in Hydrological Modelling over the Luanhe River Basin, China

Satellite precipitation products are unique sources of precipitation measurement that overcome spatial and temporal limitations, but their precision differs in specific catchments and climate zones. The purpose of this study is to evaluate the precipitation data derived from the Tropical Rainfall Measuring Mission (TRMM) 3B42RT, TRMM 3B42, and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products over the Luanhe River basin, North China, from 2001 to 2012. Subsequently, we further explore the performances of these products in hydrological models using the Soil and Water Assessment Tool (SWAT) model with parameter and prediction uncertainty analyses. The results show that 3B42 and 3B42RT overestimate precipitation, with BIAs values of 20.17% and 62.80%, respectively, while PERSIANN underestimates precipitation with a BIAs of −6.38%. Overall, 3B42 has the smallest RMSE and MAE and the highest CC values on both daily and monthly scales and performs better than PERSIANN, followed by 3B42RT. The results of the hydrological evaluation suggest that precipitation is a critical source of uncertainty in the SWAT model, and different precipitation values result in parameter uncertainty, which propagates to prediction and water resource management uncertainties. The 3B42 product shows the best hydrological performance, while PERSIANN shows unsatisfactory hydrological performance. Therefore, 3B42 performs better than the other two satellite precipitation products over the study area

Research on the Range-Frequency Interference Characteristics of Target Scattering Field in a Shallow Water Waveguide

Based on the target scattering model and the normal mode theory in a shallow water waveguide, a mathematical model of the acoustic intensity under the coupling condition of the target and the environment was deduced, and the interference striations in the monostatic and bistatic configuration were obtained by simulations. Further, a field experiment was carried out in a lake, and the data were collected for a spherical target in two frequency bands, i.e., 20−40 kHz and 40−80 kHz. The experiment results showed good agreement with the simulations. The results of the simulation and the experiment showed that the existence of the target made the interference phenomenon in shallow water waveguides more complex, and the range-frequency interference characteristics were closely related to the configuration of the sonar system, the target scattering function, the frequency range, and the target movement trajectory. These interference phenomena were found and theoretically analyzed in the paper. The research results can be applied to target detection and recognition, signal parameter estimation, target tracking, and other fields

Development of an efficient yeast platform for cannabigerolic acid biosynthesis

Cannabinoids are important therapeutical molecules for human ailments, cancer treatment, and SARS-CoV-2. The central cannabinoid, cannabigerolic acid (CBGA), is generated from geranyl pyrophosphate and olivetolic acid by Cannabis sativa prenyltransferase (CsPT4). Despite efforts to engineer microorganisms such as Saccharomyces cerevisiae (S. cerevisiae) for CBGA production, their titers remain suboptimal because of the low conversion of hexanoate into olivetolic acid and the limited activity and stability of the CsPT4. To address the low hexanoate conversion, we eliminated hexanoate consumption by the beta-oxidation pathway and reduced its incorporation into fatty acids. To address CsPT4 limitations, we expanded the endoplasmic reticulum and fused an auxiliary protein to CsPT4. Consequently, the engineered S. cerevisiae chassis showed a marked improvement of 78.64-fold in CBGA production, reaching a titer of 510.32 ± 10.70 mg l−1 from glucose and hexanoate