2,798 research outputs found
Exploring Hardware Fault Impacts on Different Real Number Representations of the Structural Resilience of TCUs in GPUs
The most recent generations of graphics processing units (GPUs) boost the execution of convolutional operations required by machine learning applications by resorting to specialized and efficient in-chip accelerators (Tensor Core Units or TCUs) that operate on matrix multiplication tiles. Unfortunately, modern cutting-edge semiconductor technologies are increasingly prone to hardware defects, and the trend to highly stress TCUs during the execution of safety-critical and high-performance computing (HPC) applications increases the likelihood of TCUs producing different kinds of failures. In fact, the intrinsic resiliency to hardware faults of arithmetic units plays a crucial role in safety-critical applications using GPUs (e.g., in automotive, space, and autonomous robotics). Recently, new arithmetic formats have been proposed, particularly those suited to neural network execution. However, the reliability characterization of TCUs supporting different arithmetic formats was still lacking. In this work, we quantitatively assessed the impact of hardware faults in TCU structures while employing two distinct formats (floating-point and posit) and using two different configurations (16 and 32 bits) to represent real numbers. For the experimental evaluation, we resorted to an architectural description of a TCU core (PyOpenTCU) and performed 120 fault simulation campaigns, injecting around 200,000 faults per campaign and requiring around 32 days of computation. Our results demonstrate that the posit format of TCUs is less affected by faults than the floating-point one (by up to three orders of magnitude for 16 bits and up to twenty orders for 32 bits). We also identified the most sensible fault locations (i.e., those that produce the largest errors), thus paving the way to adopting smart hardening solutions
A Review on Non-Volatile and Volatile Emerging Memory Technologies
As technology scaling is approaching a stand-still with architectural advancements on modern day processors struggling to improve performance, coupled with the rise in machine learning topologies demanding better performing processors, there is a pressing need to address the reasons behind today’s performance bottleneck. These reasons include long access latency of memory technologies, scalability of memory designs, energy inefficiency incurred by increased performance, and additional area overhead. To explore these issues, a holistic understanding of existing memory technologies is essential. In this chapter, a review of different memory designs starting from volatile memory technologies such as Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), NAND/NOR flash to emerging non-volatile memory technologies such as Resistive Random Access Memory (RRAM), Magneto-resistive random access memory (MRAM), Ferroelectric Field effect transistor (FeFET) is presented, with specific consideration of tradeoffs involving area, performance, energy
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Pattern recognition in the nucleation kinetics of non-equilibrium self-assembly
Inspired by biology’s most sophisticated computer, the brain, neural networks constitute a profound reformulation of computational principles. Analogous high-dimensional, highly interconnected computational architectures also arise within information-processing molecular systems inside living cells, such as signal transduction cascades and genetic regulatory networks. Might collective modes analogous to neural computation be found more broadly in other physical and chemical processes, even those that ostensibly play non-information-processing roles? Here we examine nucleation during self-assembly of multicomponent structures, showing that high-dimensional patterns of concentrations can be discriminated and classified in a manner similar to neural network computation. Specifically, we design a set of 917 DNA tiles that can self-assemble in three alternative ways such that competitive nucleation depends sensitively on the extent of colocalization of high-concentration tiles within the three structures. The system was trained in silico to classify a set of 18 grayscale 30 × 30 pixel images into three categories. Experimentally, fluorescence and atomic force microscopy measurements during and after a 150 hour anneal established that all trained images were correctly classified, whereas a test set of image variations probed the robustness of the results. Although slow compared to previous biochemical neural networks, our approach is compact, robust and scalable. Our findings suggest that ubiquitous physical phenomena, such as nucleation, may hold powerful information-processing capabilities when they occur within high-dimensional multicomponent systems
Modern computing: Vision and challenges
Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Smart Gas Sensors: Materials, Technologies, Practical ‎Applications, and Use of Machine Learning – A Review
The electronic nose, popularly known as the E-nose, that combines gas sensor arrays (GSAs) with machine learning has gained a strong foothold in gas sensing technology. The E-nose designed to mimic the human olfactory system, is used for the detection and identification of various volatile compounds. The GSAs develop a unique signal fingerprint for each volatile compound to enable pattern recognition using machine learning algorithms. The inexpensive, portable and non-invasive characteristics of the E-nose system have rendered it indispensable within the gas-sensing arena. As a result, E-noses have been widely employed in several applications in the areas of the food industry, health management, disease diagnosis, water and air quality control, and toxic gas leakage detection. This paper reviews the various sensor fabrication technologies of GSAs and highlights the main operational framework of the E-nose system. The paper details vital signal pre-processing techniques of feature extraction, feature selection, in addition to machine learning algorithms such as SVM, kNN, ANN, and Random Forests for determining the type of gas and estimating its concentration in a competitive environment. The paper further explores the potential applications of E-noses for diagnosing diseases, monitoring air quality, assessing the quality of food samples and estimating concentrations of volatile organic compounds (VOCs) in air and in food samples. The review concludes with some challenges faced by E-nose, alternative ways to tackle them and proposes some recommendations as potential future work for further development and design enhancement of E-noses
Investigation into Photon Emissions as a Side-Channel Leakage in Two Microcontrollers: A Focus on SRAM Blocks
Microcontrollers are extensively utilized across a diverse range of applications. However, with the escalating usage of these devices, the risk to their security and the valuable data they process correspondingly intensifies. These devices could potentially be susceptible to various security threats, with side channel leakage standing out as a notable concern. Among the numerous types of side-channel leakages, photon emissions from active devices emerge as a potentially significant concern. These emissions, a characteristic of all semiconductor devices including microcontrollers, occur during their operation. Depending on the operating point and the internal state of the chip, these emissions can reflect the device’s internal operations. Therefore, a malicious individual could potentially exploit these emissions to gain insights into the computations being performed within the device.
This dissertation delves into the investigation of photon emissions from the SRAM blocks of two distinct microcontrollers, utilizing a cost-effective setup. The aim is to extract information from these emissions, analyzing them as potential side-channel leakage points.
In the first segment of the study, a PIC microcontroller variant is investigated. The quiescent photon emissions from the SRAM are examined. A correlation attack was successfully executed on these emissions, which led to the recovery of the AES encryption key. Furthermore, differential analysis was used to examine the location of SRAM bits. The combination of this information with the application of an image processing method, namely the Structural Similarity Index (SSIM), assisted in revealing the content of SRAM cells from photon emission images.
The second segment of this study, for the first time, emphasizes on a RISC-V chip, examining the photon emissions of the SRAM during continuous reading. Probing the photon emissions from the row and column detectors led to the identification of a target word location, which is capable of revealing the AES key. Also, the content of target row was retrieved through the photon emissions originating from the drivers and the SRAM cells themselves. Additionally, the SSIM technique was utilized to determine the address of a targeted word in RISC-V photon emissions which cannot be analyzed through visual inspection.
The insights gained from this research contribute to a deeper understanding of side-channel leakage via photon emissions and demonstrate its potential potency in extracting critical information from digital devices. Moreover, this information significantly contributes to the development of innovative security measures, an aspect becoming increasingly crucial in our progressively digitized world
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