Deep intelligence as a service: A real-time scheduling perspective

This thesis presents a new type of cloud service, called {\em deep intelligence as a service\/}, that is expected to become increasingly common in the near future to support emerging "smart" embedded applications. This work offers a real-time scheduling model motivated by the special needs of embedded applications that use this service. A simple run-time scheduler is proposed for the server and prove an approximation bound in terms of application-perceived service utility. The service is implemented on representative device hardware and tested with a machine vision application illustrating the advantages of our scheme. The work is motivated by the proliferation of increasingly ubiquitous but resource-constrained embedded devices (often referred to as the Internet of Things -- IoT -- devices) and the growing desire to endow them with advanced interaction capabilities, such as voice recognition or machine vision. The trend suggests that machine intelligence will be increasingly offloaded to cloud or edge services that will offer advanced capabilities to the otherwise simple devices. New services will feature farms of complex trainable (or pre-trained) classifiers that client-side applications can send data to in order to gain certain types of advanced functionality, such as face recognition, voice command recognition, gesture recognition, or other. These new services will revolutionize human interaction with their physical environment, but may impose interesting real-time scheduling challenges in order to maintain responsiveness while maximizing service quality. This work includes challenges, designs for an efficient real-time scheduling algorithm for the new machine intelligence service, and evaluation on an implemented prototype

High-speed integrated lithium niobate low-index rib loaded waveguide modulator without direct lithium niobate etching

Integrated thin film lithium niobate (TFLN) modulators are emerging as an appealing choice for fiber-optic communications, data centers, and microwave photonics due to their high modulation speed and low driving voltage. The key step in fabricating integrated TFLN modulators is the high-quality etching of TFLN, which typically requires long-term fabrication process iteration and specialized equipment. Here we present an integrated TFLN modulator by incorporating low-index rib loaded waveguides onto TFLN without direct etching of TFLN. Based on our systematic investigation into the theory and design methodology of this design, we experimentally demonstrated a 1.3 cm-long Mach-Zender modulator, featuring a 3-dB bandwidth of 59 GHz and a half-wave voltage of 1.96 V. Our design significantly simplifies the fabrication process of integrated TFLN modulators and in turn opens up new avenues for the mass production of high-performance TFLN modulators at low cost

Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance. Our code is available at https://github.com/Jiaming-Liu/BayerUnifyAug.Comment: Accepted by CVPRW 201

Retrieval-Enhanced Visual Prompt Learning for Few-shot Classification

Prompt learning has become a popular approach for adapting large vision-language models, such as CLIP, to downstream tasks. Typically, prompt learning relies on a fixed prompt token or an input-conditional token to fit a small amount of data under full supervision. While this paradigm can generalize to a certain range of unseen classes, it may struggle when domain gap increases, such as in fine-grained classification and satellite image segmentation. To address this limitation, we propose Retrieval-enhanced Prompt learning (RePrompt), which introduces retrieval mechanisms to cache the knowledge representations from downstream tasks. we first construct a retrieval database from training examples, or from external examples when available. We then integrate this retrieval-enhanced mechanism into various stages of a simple prompt learning baseline. By referencing similar samples in the training set, the enhanced model is better able to adapt to new tasks with few samples. Our extensive experiments over 15 vision datasets, including 11 downstream tasks with few-shot setting and 4 domain generalization benchmarks, demonstrate that RePrompt achieves considerably improved performance. Our proposed approach provides a promising solution to the challenges faced by prompt learning when domain gap increases. The code and models will be available

Class-Specific Attention (CSA) for Time-Series Classification

Most neural network-based classifiers extract features using several hidden layers and make predictions at the output layer by utilizing these extracted features. We observe that not all features are equally pronounced in all classes; we call such features class-specific features. Existing models do not fully utilize the class-specific differences in features as they feed all extracted features from the hidden layers equally to the output layers. Recent attention mechanisms allow giving different emphasis (or attention) to different features, but these attention models are themselves class-agnostic. In this paper, we propose a novel class-specific attention (CSA) module to capture significant class-specific features and improve the overall classification performance of time series. The CSA module is designed in a way such that it can be adopted in existing neural network (NN) based models to conduct time series classification. In the experiments, this module is plugged into five start-of-the-art neural network models for time series classification to test its effectiveness by using 40 different real datasets. Extensive experiments show that an NN model embedded with the CSA module can improve the base model in most cases and the accuracy improvement can be up to 42%. Our statistical analysis show that the performance of an NN model embedding the CSA module is better than the base NN model on 67% of MTS and 80% of UTS test cases and is significantly better on 11% of MTS and 13% of UTS test cases.Comment: 12 page