Techniques for Aging, Soft Errors and Temperature to Increase the Reliability of Embedded On-Chip Systems

This thesis investigates the challenge of providing an abstracted, yet sufficiently accurate reliability estimation for embedded on-chip systems. In addition, it also proposes new techniques to increase the reliability of register files within processors against aging effects and soft errors. It also introduces a novel thermal measurement setup that perspicuously captures the infrared images of modern multi-core processors

Multi-shape symmetric encryption mechanism for nongeneric attacks mitigation

Static cyphers use static transformations for encryption and decryption. Therefore, the attacker will have some knowledge that can be exploited to construct assaults since the transformations are static. The class of attacks which target a specific cypher design are called Non-Generic Attacks. Whereby, dynamic cyphers can be utilised to mitigate non-generic attacks. Dynamic cyphers aim at mitigating non-generic attacks by changing how the cyphers work according to the value of the encryption key. However, existing dynamic cyphers either degrade the performance or decrease the cypher’s actual security. Hence, this thesis introduces a Multi-Shape Symmetric Encryption Mechanism (MSSEM) which is capable of mitigating non-generic attacks by eliminating the opponents’ leverage of accessing the exact operation details. The base cyphers that have been applied in the proposed MSSEM are the Advanced Encryption Standard (AES) competition finalists, namely Rijndael, Serpent, MARS, Twofish, and RC6. These cyphers satisfy three essential criteria, such as security, performance, and expert input. Moreover, the modes of operation used by the MSSEM are the secure modes suggested by the National Institute of Standards and Technology, namely, Cipher Block Chaining (CBC), Cipher Feedback Mode (CFB), Output Feedback Mode (OFB), and Counter (CTR). For the proposed MSSEM implementation, the sender initially generates a random key using a pseudorandom number generator such as Blum Blum Shub (BBS) or a Linear Congruential Generator (LCG). Subsequently, the sender securely shares the key with the legitimate receiver. Besides that, the proposed MSSEM has an entity called the operation table that includes sixty different cypher suites. Each cypher suite has a specific cypher and mode of operation. During the run-time, one cypher suite is randomly selected from the operation table, and a new key is extracted from the master key with the assistance of SHA-256. The suite, as well as the new key, is allowed to encrypt one message. While each of the messages produces a new key and cypher suite. Thus, no one except communicating parties can access the encryption keys or the cypher suites. Furthermore, the security of MSSEM has been evaluated and mathematically proven to resist known and unknown attacks. As a result, the proposed MSSEM successfully mitigates unknown non-generic attacks by a factor of 2−6. In addition, the proposed MSSEM performance is better than MODEM since MODEM generates 4650 milliseconds to encrypt approximately 1000 bytes, whereas MSSEM needs only 0.14 milliseconds. Finally, a banking system simulation has been tested with the proposed MSSEM in order to secure inbound and outbound system traffic

Cross-Layer Optimization for Power-Efficient and Robust Digital Circuits and Systems

With the increasing digital services demand, performance and power-efficiency become vital requirements for digital circuits and systems. However, the enabling CMOS technology scaling has been facing significant challenges of device uncertainties, such as process, voltage, and temperature variations. To ensure system reliability, worst-case corner assumptions are usually made in each design level. However, the over-pessimistic worst-case margin leads to unnecessary power waste and performance loss as high as 2.2x. Since optimizations are traditionally confined to each specific level, those safe margins can hardly be properly exploited. To tackle the challenge, it is therefore advised in this Ph.D. thesis to perform a cross-layer optimization for digital signal processing circuits and systems, to achieve a global balance of power consumption and output quality. To conclude, the traditional over-pessimistic worst-case approach leads to huge power waste. In contrast, the adaptive voltage scaling approach saves power (25% for the CORDIC application) by providing a just-needed supply voltage. The power saving is maximized (46% for CORDIC) when a more aggressive voltage over-scaling scheme is applied. These sparsely occurred circuit errors produced by aggressive voltage over-scaling are mitigated by higher level error resilient designs. For functions like FFT and CORDIC, smart error mitigation schemes were proposed to enhance reliability (soft-errors and timing-errors, respectively). Applications like Massive MIMO systems are robust against lower level errors, thanks to the intrinsically redundant antennas. This property makes it applicable to embrace digital hardware that trades quality for power savings.Comment: 190 page

NDELS: A Novel Approach for Nighttime Dehazing, Low-Light Enhancement, and Light Suppression

This paper tackles the intricate challenge of improving the quality of nighttime images under hazy and low-light conditions. Overcoming issues including nonuniform illumination glows, texture blurring, glow effects, color distortion, noise disturbance, and overall, low light have proven daunting. Despite the inherent difficulties, this paper introduces a pioneering solution named Nighttime Dehazing, Low-Light Enhancement, and Light Suppression (NDELS). NDELS utilizes a unique network that combines three essential processes to enhance visibility, brighten low-light regions, and effectively suppress glare from bright light sources. In contrast to limited progress in nighttime dehazing, unlike its daytime counterpart, NDELS presents a comprehensive and innovative approach. The efficacy of NDELS is rigorously validated through extensive comparisons with eight state-of-the-art algorithms across four diverse datasets. Experimental results showcase the superior performance of our method, demonstrating its outperformance in terms of overall image quality, including color and edge enhancement. Quantitative (PSNR, SSIM) and qualitative metrics (CLIPIQA, MANIQA, TRES), measure these results

A Study of User Perception of the Quality of Video Content Rendered Inside a 3-D Virtual Environment

© 2016 IEEE. This paper reports on the result of a user study to assess the impact of resolution and frame rate of video on the quality of experience of the users, when the video is rendered inside a 3-D virtual space, and consequently viewed from arbitrary perspectives. A mathematical model for video rate is presented that expresses the total rate as the product of separate functions of spatial and temporal resolutions. Results from the user study are combined with the model to predict the rate parameters which will result in perceptually acceptable quality using the 3-D features of the virtual environment. The results show that by exploiting the insensitivity of users to controlled quality degradation, the downstream network load for the client can be significantly reduced with little or no perceptual impact on the clients

Generating Robust Adversarial Examples against Online Social Networks (OSNs)

Online Social Networks (OSNs) have blossomed into prevailing transmission channels for images in the modern era. Adversarial examples (AEs) deliberately designed to mislead deep neural networks (DNNs) are found to be fragile against the inevitable lossy operations conducted by OSNs. As a result, the AEs would lose their attack capabilities after being transmitted over OSNs. In this work, we aim to design a new framework for generating robust AEs that can survive the OSN transmission; namely, the AEs before and after the OSN transmission both possess strong attack capabilities. To this end, we first propose a differentiable network termed SImulated OSN (SIO) to simulate the various operations conducted by an OSN. Specifically, the SIO network consists of two modules: 1) a differentiable JPEG layer for approximating the ubiquitous JPEG compression and 2) an encoder-decoder subnetwork for mimicking the remaining operations. Based upon the SIO network, we then formulate an optimization framework to generate robust AEs by enforcing model outputs with and without passing through the SIO to be both misled. Extensive experiments conducted over Facebook, WeChat and QQ demonstrate that our attack methods produce more robust AEs than existing approaches, especially under small distortion constraints; the performance gain in terms of Attack Success Rate (ASR) could be more than 60%. Furthermore, we build a public dataset containing more than 10,000 pairs of AEs processed by Facebook, WeChat or QQ, facilitating future research in the robust AEs generation. The dataset and code are available at https://github.com/csjunjun/RobustOSNAttack.git.Comment: 26 pages, 9 figure

Digital design techniques for dependable High-Performance Computing

L'abstract è presente nell'allegato / the abstract is in the attachmen