Robust and Scalable Hyperdimensional Computing With Brain-Like Neural Adaptations

The Internet of Things (IoT) has facilitated many applications utilizing edge-based machine learning (ML) methods to analyze locally collected data. Unfortunately, popular ML algorithms often require intensive computations beyond the capabilities of today's IoT devices. Brain-inspired hyperdimensional computing (HDC) has been introduced to address this issue. However, existing HDCs use static encoders, requiring extremely high dimensionality and hundreds of training iterations to achieve reasonable accuracy. This results in a huge efficiency loss, severely impeding the application of HDCs in IoT systems. We observed that a main cause is that the encoding module of existing HDCs lacks the capability to utilize and adapt to information learned during training. In contrast, neurons in human brains dynamically regenerate all the time and provide more useful functionalities when learning new information. While the goal of HDC is to exploit the high-dimensionality of randomly generated base hypervectors to represent the information as a pattern of neural activity, it remains challenging for existing HDCs to support a similar behavior as brain neural regeneration. In this work, we present dynamic HDC learning frameworks that identify and regenerate undesired dimensions to provide adequate accuracy with significantly lowered dimensionalities, thereby accelerating both the training and inference.Comment: arXiv admin note: substantial text overlap with arXiv:2304.0550

Runtime Adaptive System-on-Chip Communication Architecture

The adaptive system provides adaptivity both in the system-level and in the architecture-level. The system-level adaptation is provided using a runtime application mapping. The architecture-level adaptation is implemented by using several novel methodologies to increase the resource utilization of the underlying silicon fabric, i.e. sharing the Virtual Channel Buffers among different output ports. To achieve successful runtime adaptation, a runtime observability infrastructure is included

DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data

With the rapid evolution of the Internet of Things, many real-world applications utilize heterogeneously connected sensors to capture time-series information. Edge-based machine learning (ML) methodologies are often employed to analyze locally collected data. However, a fundamental issue across data-driven ML approaches is distribution shift. It occurs when a model is deployed on a data distribution different from what it was trained on, and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) have been proposed to capture spatial and temporal dependencies in multi-sensor time series data, requiring intensive computational resources beyond the capacity of today's edge devices. While brain-inspired hyperdimensional computing (HDC) has been introduced as a lightweight solution for edge-based learning, existing HDCs are also vulnerable to the distribution shift challenge. In this paper, we propose DOMINO, a novel HDC learning framework addressing the distribution shift problem in noisy multi-sensor time-series data. DOMINO leverages efficient and parallel matrix operations on high-dimensional space to dynamically identify and filter out domain-variant dimensions. Our evaluation on a wide range of multi-sensor time series classification tasks shows that DOMINO achieves on average 2.04% higher accuracy than state-of-the-art (SOTA) DNN-based domain generalization techniques, and delivers 16.34x faster training and 2.89x faster inference. More importantly, DOMINO performs notably better when learning from partially labeled and highly imbalanced data, providing 10.93x higher robustness against hardware noises than SOTA DNNs

Romanus: Robust Task Offloading in Modular Multi-Sensor Autonomous Driving Systems

Due to the high performance and safety requirements of self-driving applications, the complexity of modern autonomous driving systems (ADS) has been growing, instigating the need for more sophisticated hardware which could add to the energy footprint of the ADS platform. Addressing this, edge computing is poised to encompass self-driving applications, enabling the compute-intensive autonomy-related tasks to be offloaded for processing at compute-capable edge servers. Nonetheless, the intricate hardware architecture of ADS platforms, in addition to the stringent robustness demands, set forth complications for task offloading which are unique to autonomous driving. Hence, we present $ROMANUS$, a methodology for robust and efficient task offloading for modular ADS platforms with multi-sensor processing pipelines. Our methodology entails two phases: (i) the introduction of efficient offloading points along the execution path of the involved deep learning models, and (ii) the implementation of a runtime solution based on Deep Reinforcement Learning to adapt the operating mode according to variations in the perceived road scene complexity, network connectivity, and server load. Experiments on the object detection use case demonstrated that our approach is 14.99% more energy-efficient than pure local execution while achieving a 77.06% reduction in risky behavior from a robust-agnostic offloading baseline.Comment: This paper has been accepted to the 2022 International Conference On Computer-Aided Design (ICCAD 2022

BayesImposter: Bayesian Estimation Based .bss Imposter Attack on Industrial Control Systems

Over the last six years, several papers used memory deduplication to trigger various security issues, such as leaking heap-address and causing bit-flip in the physical memory. The most essential requirement for successful memory deduplication is to provide identical copies of a physical page. Recent works use a brute-force approach to create identical copies of a physical page that is an inaccurate and time-consuming primitive from the attacker's perspective. Our work begins to fill this gap by providing a domain-specific structured way to duplicate a physical page in cloud settings in the context of industrial control systems (ICSs). Here, we show a new attack primitive - \textit{BayesImposter}, which points out that the attacker can duplicate the .bss section of the target control DLL file of cloud protocols using the \textit{Bayesian estimation} technique. Our approach results in less memory (i.e., 4 KB compared to GB) and time (i.e., 13 minutes compared to hours) compared to the brute-force approach used in recent works. We point out that ICSs can be expressed as state-space models; hence, the \textit{Bayesian estimation} is an ideal choice to be combined with memory deduplication for a successful attack in cloud settings. To demonstrate the strength of \textit{BayesImposter}, we create a real-world automation platform using a scaled-down automated high-bay warehouse and industrial-grade SIMATIC S7-1500 PLC from Siemens as a target ICS. We demonstrate that \textit{BayesImposter} can predictively inject false commands into the PLC that can cause possible equipment damage with machine failure in the target ICS. Moreover, we show that \textit{BayesImposter} is capable of adversarial control over the target ICS resulting in severe consequences, such as killing a person but making it looks like an accident. Therefore, we also provide countermeasures to prevent the attack

Golden Reference-Free Hardware Trojan Localization using Graph Convolutional Network

The globalization of the Integrated Circuit (IC) supply chain has moved most of the design, fabrication, and testing process from a single trusted entity to various untrusted third-party entities worldwide. The risk of using untrusted third-Party Intellectual Property (3PIP) is the possibility for adversaries to insert malicious modifications known as Hardware Trojans (HTs). These HTs can compromise the integrity, deteriorate the performance, deny the service, and alter the functionality of the design. While numerous HT detection methods have been proposed in the literature, the crucial task of HT localization is overlooked. Moreover, a few existing HT localization methods have several weaknesses: reliance on a golden reference, inability to generalize for all types of HT, lack of scalability, low localization resolution, and manual feature engineering/property definition. To overcome their shortcomings, we propose a novel, golden reference-free HT localization method at the pre-silicon stage by leveraging Graph Convolutional Network (GCN). In this work, we convert the circuit design to its intrinsic data structure, graph and extract the node attributes. Afterward, the graph convolution performs automatic feature extraction for nodes to classify the nodes as Trojan or benign. Our automated approach does not burden the designer with manual code review. It locates the Trojan signals with 99.6% accuracy, 93.1% F1-score, and a false-positive rate below 0.009%.Comment: IEEE Transactions on Very Large Scale Integration Systems (TVLSI), 202

Underlying the Causes and Impact of Crime Victimization: A Study of Urban Area in Bangladesh

In Bangladesh, like all other countries of the world Crime victimization has become a frightening, threatening and unsettling experience for many people. This study tries to trace the causes of different kinds of crime victimization and also tries to find out its impact on victims in relation to urban environment. Based on literature relate to victimization and collecting primary data from urban area’s victim, this study will enable researchers to explore the prevalence of crime in urban area and to identify the causes and impact of crime victimization on victims by analysing the demographic status of victim-offender, measuring the causal variables and several cost likely financial, physical etc. The subject of this study was composed of 3957 respondent’s selected from 12th city corporations including Dhaka city followed by probability sampling method for collecting information from the general peoples who have victimized. The study revealed that two-third of the offenders was unknown to the victim. The most common cause of victimization includes self-blaming (lack of awareness or carelessness) remained at the top reason, which constitutes 31 percent of the victim. This victimization has affected victims psychologically, physically and financially including several losses. The perceptions gathered through this study will helps to take important measures and strategies to ensure safe livelihood as well as increase the performance of the law enforcement agencies