Robust Efficient Edge AI: New Principles and Frameworks for Empowering Artificial Intelligence on Edge Devices

Deep learning has revolutionised a breadth of industries by automating critical tasks while achieving superhuman accuracy. However, many of these benefits are driven by huge neural networks deployed on cloud servers that consume enormous energy. This thesis contributes two classes of novel frameworks and algorithms that extend the deployment frontier of deep learning models to tiny edge devices, which commonly operate in noisy environments with limited compute footprints: (1) New frameworks for efficient edge AI. We introduce methods that reduce inference cost through filter pruning and efficient network design. CUP presents a new method for compressing and accelerating models, by clustering and pruning similar filters in each layer. CMPNAS presents a new visual search framework that optimises a small and efficient edge model to work in tandem with a large server model to achieve high accuracy, achieving up to 80x compute cost reduction. (2) New methods for robust edge AI. We Introduce new methods that enable robustness to real-world noise while reducing inference cost. REST extends the scope of pruning to obtain networks that are 9x more efficient, run 5x faster and robust to adversarial and gaussian noise. HAR generalises the idea of early exiting in multi-branch neural networks to the training phase leading to networks that obtain state-of-the-art accuracy under class imbalance while saving up to 20% inference compute. IMBNAS optimises neural architectures on imbalanced datasets through super-network adaptation strategies that lead to 5x compute savings compared to searching from scratch. Our work makes a significant impact to industry and society: CMPNAS enables the edge deployment use-case for fashion and face retrieval services, and was highlighted at Amazon company-wide to thousands of researchers and developers. REST enables at-home sleep monitoring through a mobile phone and was highlighted by several news media.Ph.D

MalNet: A Large-Scale Cybersecurity Image Database of Malicious Software

Computer vision is playing an increasingly important role in automated malware detection with to the rise of the image-based binary representation. These binary images are fast to generate, require no feature engineering, and are resilient to popular obfuscation methods. Significant research has been conducted in this area, however, it has been restricted to small-scale or private datasets that only a few industry labs and research teams have access to. This lack of availability hinders examination of existing work, development of new research, and dissemination of ideas. We introduce MalNet, the largest publicly available cybersecurity image database, offering 133x more images and 27x more classes than the only other public binary-image database. MalNet contains over 1.2 million images across a hierarchy of 47 types and 696 families. We provide extensive analysis of MalNet, discussing its properties and provenance. The scale and diversity of MalNet unlocks new and exciting cybersecurity opportunities to the computer vision community--enabling discoveries and research directions that were previously not possible. The database is publicly available at www.mal-net.org

REST: Robust and Efficient Neural Networks for Sleep Monitoring in the Wild

In recent years, significant attention has been devoted towards integrating deep learning technologies in the healthcare domain. However, to safely and practically deploy deep learning models for home health monitoring, two significant challenges must be addressed: the models should be (1) robust against noise; and (2) compact and energy-efficient. We propose REST, a new method that simultaneously tackles both issues via 1) adversarial training and controlling the Lipschitz constant of the neural network through spectral regularization while 2) enabling neural network compression through sparsity regularization. We demonstrate that REST produces highly-robust and efficient models that substantially outperform the original full-sized models in the presence of noise. For the sleep staging task over single-channel electroencephalogram (EEG), the REST model achieves a macro-F1 score of 0.67 vs. 0.39 achieved by a state-of-the-art model in the presence of Gaussian noise while obtaining 19x parameter reduction and 15x MFLOPS reduction on two large, real-world EEG datasets. By deploying these models to an Android application on a smartphone, we quantitatively observe that REST allows models to achieve up to 17x energy reduction and 9x faster inference. We open-source the code repository with this paper: https://github.com/duggalrahul/REST.Comment: Accepted to WWW 202

Identification of Hot and Cold spots in genome of Mycobacterium tuberculosis using Shewhart Control Charts

The organization of genomic sequences is dynamic and undergoes change during the process of evolution. Many of the variations arise spontaneously and the observed genomic changes can either be distributed uniformly throughout the genome or be preferentially localized to some regions (hot spots) compared to others. Conversely cold spots may tend to accumulate very few variations or none at all. In order to identify such regions statistically, we have developed a method based on Shewhart Control Chart. The method was used for identification of hot and cold spots of single-nucleotide variations (SNVs) in Mycobacterium tuberculosis genomes. The predictions have been validated by sequencing some of these regions derived from clinical isolates. This method can be used for analysis of other genome sequences particularly infectious microbes

Robust Principles: Architectural Design Principles for Adversarially Robust CNNs

Our research aims to unify existing works' diverging opinions on how architectural components affect the adversarial robustness of CNNs. To accomplish our goal, we synthesize a suite of three generalizable robust architectural design principles: (a) optimal range for depth and width configurations, (b) preferring convolutional over patchify stem stage, and (c) robust residual block design through adopting squeeze and excitation blocks and non-parametric smooth activation functions. Through extensive experiments across a wide spectrum of dataset scales, adversarial training methods, model parameters, and network design spaces, our principles consistently and markedly improve AutoAttack accuracy: 1-3 percentage points (pp) on CIFAR-10 and CIFAR-100, and 4-9 pp on ImageNet. The code is publicly available at https://github.com/poloclub/robust-principles.Comment: Published at BMVC'2

2017 Robotic Instrument Segmentation Challenge

In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison. However, this type of approach has had limited translation to problems in robotic assisted surgery as this field has never established the same level of common datasets and benchmarking methods. In 2015 a sub-challenge was introduced at the EndoVis workshop where a set of robotic images were provided with automatically generated annotations from robot forward kinematics. However, there were issues with this dataset due to the limited background variation, lack of complex motion and inaccuracies in the annotation. In this work we present the results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments

Different Modes of Retrovirus Restriction by Human APOBEC3A and APOBEC3G In Vivo

The apolipoprotein B editing complex 3 (A3) cytidine deaminases are among the most highly evolutionarily selected retroviral restriction factors, both in terms of gene copy number and sequence diversity. Primate genomes encode seven A3 genes, and while A3F and 3G are widely recognized as important in the restriction of HIV, the role of the other genes, particularly A3A, is not as clear. Indeed, since human cells can express multiple A3 genes, and because of the lack of an experimentally tractable model, it is difficult to dissect the individual contribution of each gene to virus restriction in vivo. To overcome this problem, we generated human A3A and A3G transgenic mice on a mouse A3 knockout background. Using these mice, we demonstrate that both A3A and A3G restrict infection by murine retroviruses but by different mechanisms: A3G was packaged into virions and caused extensive deamination of the retrovirus genomes while A3A was not packaged and instead restricted infection when expressed in target cells. Additionally, we show that a murine leukemia virus engineered to express HIV Vif overcame the A3G-mediated restriction, thereby creating a novel model for studying the interaction between these proteins. We have thus developed an in vivo system for understanding how human A3 proteins use different modes of restriction, as well as a means for testing therapies that disrupt HIV Vif-A3G interactions.United States. Public Health Service (Grant R01-AI-085015)United States. Public Health Service (Grant T32-CA115299 )United States. Public Health Service (Grant F32-AI100512

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe