Siew Kei Lam

Nanyang Technological University | ntu · Hardware and Embedded Systems (HES)

In vehicular networks, task scheduling at the microarchitecture-level and network-level offers tremendous potential to improve the quality of computing services for deep neural network (DNN) inference. However, existing task scheduling works only focus on either one of the two levels, which results in inefficient utilization of computing resources....

Civil infrastructure (e.g., buildings, roads, underground tunnels) could lose its expected physical and functional conditions after years of operation. Timely and accurate inspection and assessment of such infrastructures are essential to ensure safety and serviceability, e.g., by preventing unsafe working conditions and hazards. Cracks, which are...

By introducing collision information into divide-and-conquer distinguishers, the existing collision-optimized side-channel attacks transform the given candidate space into a significantly smaller collision space, thus achieving more efficient key recovery. However, the candidates of the first several sub-keys shared by collision chains are still re...

In this paper, we present a novel hardware trojan assisted side-channel attack to reverse engineer DNN architectures on edge FPGA accelerators. In particular, our attack targets the widely-used Versatile Tensor Accelerator (VTA). A hardware trojan is employed to track the memory transactions by monitoring the AXI interface signals of VTA’s submodul...

Multimodal public transport networks (MMPTNs) in modern cities are becoming increasingly complex. This makes finding optimal journey routes challenging due to a large number of transfer options that need to be properly considered. Furthermore, the complexity of the problem is compounded when multiple conflicting travel criteria are considered (e.g....

Existing visual SLAM (vSLAM) systems fail to perform well in dynamic environments as they cannot effectively ignore moving objects during pose estimation and mapping. We propose a lightweight approach to improve the robustness of existing feature based RGB-D and stereo vSLAM by accurately removing dynamic outliers in the scene that contribute to fa...

Existing pedestrian detection methods suffer from performance degradation in the presence of small-scale pedestrians who are positioned at far distance from the camera. We present a pedestrian detection framework that is not only robust to small- and large-scale pedestrians, but is also significantly faster than state-of-the-art methods. The propos...

Ant Colony Optimization (ACO) algorithms have been widely employed for solving optimization problems. Their ability to find optimal solutions depends heavily on the parameterization of the pheromone trails. However, the pheromone parameterization mechanisms in existing ACO algorithms have two major shortcomings: 1) pheromone trails are instance-spe...

Short-term traffic prediction (e.g., less than 15 min) is challenging due to severe fluctuations of traffic data caused by dynamic traffic conditions and uncertainties (e.g., in data acquisition, driver behaviors, etc.). Substantial efforts have been undertaken to incorporate spatiotemporal correlations for improving traffic prediction accuracy. In...

Self-supervised label augmentation has emerged as an effective means to overcome the data scarcity problem for supervised vision tasks. Existing rotation-based self-supervised label augmentation methods either impose or relax the rotation invariance constraint on the primary classifier, which omit necessary supervisory information and may degrade t...

Deep convolutional neural network compression has attracted lots of attention due to the need to deploy accurate models on resource-constrained edge devices. Existing techniques mostly focus on compressing networks for image-level classification, and it is not clear if they generalize well on network architectures for more challenging pixel-level t...

Traffic congestion has become a global concern due to continuous increase in traffic demand and limited road capacity. The ability to predict traffic congestion propagation, which depicts the spatiotemporal evolution of the congestion scenario, is essential for developing smart traffic management systems and enabling road users to make informed rou...

Pedestrian trajectory prediction is an active research area with recent works undertaken to embed accurate models of pedestrians social interactions and their contextual compliance into dynamic spatial graphs. However, existing works rely on spatial assumptions about the scene and dynamics, which entails a significant challenge to adapt the graph s...

Many efforts are devoted to predicting congestion evolution using propagation patterns that are mined from historical traffic data. However, the prediction quality is limited to the intrinsic properties that are present in the mined patterns. In addition, these mined patterns frequently fail to sufficiently capture many realistic characteristics of...

Substantial efforts have been devoted to the investigation of spatiotemporal correlations for improving traffic speed prediction accuracy. However, existing works typically model the correlations based solely on the observed traffic state (e.g. traffic speed) without due consideration that different correlation measurements of the traffic data coul...

First-mile transportation provides convenient transit service for passengers to travel from their homes, workplaces, or public institutions to a public transit station that is located beyond comfortable walking distance. This paper studies the Passenger-Centric Vehicle Routing for First-Mile Transportation (PCVR-FMT) problem to plan optimal vehicle...

Mobile Edge Computing (MEC) infrastructure raises security concerns when the shared resources involve sensitive and private data of users. This paper proposes a novel blockchain-based key management scheme for MEC that is essential for ensuring secure group communication among the devices. In the proposed scheme, when a mobile device joins a subnet...

In this paper, we present area-time efficient reconfigurable architectures for the implementation of the integer discrete cosine transform (DCT), which supports all the transform lengths to be used in High Efficiency Video Coding (HEVC). We propose three 1D reconfigurable architectures that can be configured for the computation of the DCT of any of...

Network pruning for deep convolutional neural networks (CNNs) has recently achieved notable research progress on image-level classification. However, most existing pruning methods are not catered to or evaluated on semantic segmentation networks. In this paper, we advocate the importance of contextual information during channel pruning for semantic...

Pedestrian trajectory prediction is an active research area with recent works undertaken to embed accurate models of pedestrians social interactions and their contextual compliance into dynamic spatial graphs. However, existing works rely on spatial assumptions about the scene and dynamics, which entails a significant challenge to adapt the graph s...

An accurate leakage model is critical to side-channel attacks and evaluations. Leakage certification plays an important role to address the following question: “how good is my leakage model?” Moreover, most of the current leakage model profiling only exploits the information from lower orders of moments. They still need to tolerate assumption error...

Malware detection is still one of the difficult problems in computer security because of the daily occurrences of newer varieties of malware programs. There have been enormous efforts in developing a generalized solution to this critical security aspect, but a little has been done considering the security of resource constraint embedded devices. In...

Accurately detecting the presence and evolving boundaries of cracks on rock surfaces is critical for understanding the behavior of crack evolutions and facture mechanism of rock and rock-like material, which could cause engineering disasters if proper operation were not taken to deal with the evolving cracks. In this paper, we investigate the probl...

Detection of malicious programs using hardware-based features has gained prominence recently. The tamper-resistant hardware metrics prove to be a better security feature than the high-level software metrics, which can be easily obfuscated. Hardware Performance Counters (HPC), which are inbuilt in most of the recent processors, are often the choice...

Recovering keys ranked in very deep candidate space efficiently is a very important but challenging issue in Side-Channel Attacks (SCAs). State-of-the-art Collision-Optimized Divide-and-Conquer Attacks (CODCAs) extract collision information from a collision attack to optimize the key recovery of a divide-and-conquer attack, and transform the very h...

We present a unified multi-task learning architecture for fast and accurate pedestrian detection. Different from existing methods which often focus on either a new loss function or architecture, we propose an improved multi-task convolutional neural network learning architecture to effectively and efficiently interfuse the task of pedestrian detect...

Conventional deep learning models are trained once and deployed. However, models deployed in agents operating in dynamic environments need to constantly acquire new knowledge, while preventing catastrophic forgetting of previous knowledge. This ability is commonly referred to as lifelong learning. In this paper, we address the performance and resou...

Several combined attacks have shown promising results in recovering cryptographic keys by introducing collision information into divide-and-conquer attacks to transform a part of the best key candidates within given thresholds into a much smaller collision space. However, these Collision-Optimized Divide-and-Conquer Attacks (CODCAs) uniformly demar...

Transport mode identification (TMI), which infers the travel modes of user trajectories, is essential to facilitate an understanding of urban mobility patterns and passengers' choice behaviors with the goal of improving urban transportation systems. To achieve higher accuracy, existing TMI methods usually rely on mobility features obtained from den...

Pedestrian trajectory prediction is a prominent research track that has advanced towards modelling of crowd social and contextual interactions, with extensive usage of Long Short-Term Memory (LSTM) for temporal representation of walking trajectories. Existing approaches use virtual neighborhoods as a fixed grid for pooling social states of pedestri...

By introducing collision information into divide-and-conquer attacks, several existing works transform the original candidate space, which may be too large to enumerate, into a significantly smaller collision space, making key recovery possible. However, the use of inefficient collision detection algorithms and fault tolerance mechanisms make them...

Humans have a remarkable ability to learn continuously from th e environment and inner experience. One of the grand goals of robots is to build an artificial "lifelong learning" agent that can shape a cultivated understanding of the world from the current scene and previous knowledge via an autonomous lifelong development. It is challenging for the...

Hardware/software (HW/SW) partitioning, that decides which components of an application are implemented in hardware and which ones in software, is a crucial step in embedded system design. On modern heterogeneous embedded system platform, each component of application can typically have multiple feasible configurations/implementations, trading off...

This report summarizes IROS 2019-Lifelong Robotic Vision Competition (Lifelong Object Recognition Challenge) with methods and results from the top $8$ finalists (out of over~$150$ teams). The competition dataset (L)ifel(O)ng (R)obotic V(IS)ion (OpenLORIS) - Object Recognition (OpenLORIS-object) is designed for driving lifelong/continual learning re...

Intelligent vehicles and social robots need to navigate in crowded environments while avoiding collisions with pedestrians. To achieve this, pedestrian trajectory prediction is essential. However, predicting pedestrians' trajectory in crowded environments is nontrivial as human-to-human interactions among the crowd participants influence their moti...

Acquiring accurate dense depth maps with low computational complexity is crucial for real-time applications that require 3D reconstruction. The current sensors capable of generating dense maps are expensive and bulky, while compact low-cost sensors can only generate the sparse map measurements reliably. To overcome this predicament, we propose an e...

An important prerequisite for Side-Channel Attacks (SCA) is leakage sampling where the side-channel measurements (i.e. power traces) of the cryptographic device are collected for further analysis. However, as the operating frequency of cryptographic devices continues to increase due to advancing technology, leakage sampling will impose higher requi...

Corner detection plays an essential role in many computer vision applications, e.g., object recognition, motion analysis and stereo matching. Several hardware implementations of corner detection algorithms have been previously reported to meet the real-time requirements of such applications. However, most of the reported implementations adopt simil...

Pedestrian detection has achieved notable progress in the field of computer vision over the past decade. However, existing top-performing approaches suffer from high computational complexity which prohibits their realization on embedded platforms with low computational capabilities. In this paper, we propose a robust and fast pedestrian detection f...

In this paper, we address the problem of travel time prediction of bus journeys which consist of bus riding times (may involve multiple bus services) and also the waiting times at transfer points. We propose a novel method called Traffic Pattern centric Segment Coalescing Framework (TP-SCF) that relies on learned disparate patterns of traffic condi...

The task offloading problem, which aims to balance the energy consumption and latency for Mobile Edge Computing (MEC), is still a challenging problem due to the dynamic changing system environment. To reduce energy while guaranteeing delay constraint for mobile applications, we propose an access control management architecture for 5G heterogeneous...

Cloudlet deployment and resource allocation for mobile users (MUs) have been extensively studied in existing works for computation resource scarcity. However, most of them failed to jointly consider the two techniques together, and the selfishness of cloudlet and access point (AP) are ignored. Inspired by the group-buying mechanism, this paper prop...

The first-mile transportation provides a transit service using ridesharing-based vehicles, e.g., feeder buses, for passengers to travel from their homes, workplaces, or public institutions to the nearest public transportation depots (rapid-transit metro or appropriated bus stations) which are located beyond comfortable walking distance. This paper...

The adoption of large-scale MPSoCs and the globalization of the IC design flow give rise to two major concerns: high power density due to continuous technology scaling and security due to the untrustworthiness of the third-party intellectual property (3PIP) cores. However, little work has been undertaken to consider these two critical issues jointl...

Integrity trees are widely used in computer systems to prevent replay, splicing, and spoofing attacks on memories. Such mechanisms incur excessive performance and energy overhead. We propose a memory authentication framework that combines architecture-specific optimizations of the integrity tree with mechanisms that enable it to restructure at runt...

Pedestrian detection plays an important role in many applications such as autonomous driving. We propose a method that explores semantic segmentation results as self-attention cues to significantly improve the pedestrian detection performance. Specifically, a multi-task network is designed to jointly learn semantic segmentation and pedestrian detec...

Pedestrian trajectory prediction is essential for collision avoidance in autonomous driving and robot navigation. However, predicting a pedestrian's tra-jectory in crowded environments is non-trivial as it is influenced by other pedestrians' motion and static structures that are present in the scene. Such human-human and human-space interactions le...

Program obfuscation is widely used to protect commercial software against reverse-engineering. However, an adversary can still download, disassemble and analyze binaries of the obfuscated code executed on an embedded System-on-Chip (SoC), and by correlating execution times to input values, extract secret information from the program. In this paper,...

Accurate travel-time prediction of public transport is essential for reliable journey planning in urban transportation systems. However, existing studies on bus travel-/arrival-time prediction often focus only on improving the prediction accuracy of a single bus trip. This is inadequate in modern public transportation systems, where a bus journey u...

Feature matching is a fundamental step in many real-time computer vision applications such as simultaneous localization and mapping, motion analysis, and stereo correspondence. The performance of these applications depends on the distinctiveness of the visual feature descriptors used, and the speed at which they can be extracted from video frames....

Timing side-channel attacks pose a major threat to embedded systems due to their ease of accessibility. We propose CIDPro, a framework that relies on dynamic program diversification to mitigate timing side-channel leakage. The proposed framework integrates the widely used LLVM compiler infrastructure and the increasingly popular RISC-V FPGA soft-pr...

Timing side-channel attacks pose a major threat to embedded systems due to their ease of accessibility. We propose CIDPro, a framework that relies on dynamic program diversification to mitigate timing side-channel leakage. The proposed framework integrates the widely used LLVM compiler infrastructure and the increasingly popular RISC-V FPGA soft-pr...

Slides of the oral presentation of the corresponding paper (https://www.researchgate.net/publication/327495855_CIDPro_Custom_Instructions_for_Dynamic_Program_Diversification)

This paper investigates the techniques to construct high-quality target processor array (fault-free logical subarray) from a physical array with faulty processing elements (PEs), where a fixed number of spare PEs are pre-integrated that can be used to replace the faulty ones when necessary. A reconfiguration algorithm is successfully developed base...

The combination of FAST corners and BRIEF descriptors provide highly robust image features. We present a novel detector for computing the FAST-BRIEF features from streaming images. To reduce the complexity of the BRIEF descriptor, we employ an optimized adder tree to perform summation by accumulation on streaming pixels for the smoothing operation....

RowHammer attacks pose a security threat to DRAM chips by causing bit-flips in sensitive memory regions. We propose a technique that combines a sliding window protocol and a dynamic integrity tree to rapidly detect multiple bit-flips caused by RowHammer attacks. Sliding window protocol monitors the frequent accesses made to the same bank in short i...

Robust automatic pavement crack detection is critical to automated road condition evaluation. However, research on crack detection is still limited and pixel-level automatic crack detection remains a challenging problem, due to heterogeneous pixel intensity, complex crack topology, poor illumination condition, and noisy texture background. In this...

Programmable Systems-on-Chips (SoCs) are expected to incorporate a larger number of application-specific hardware accelerators with tightly integrated memories in order to meet stringent performance-power requirements of embedded systems. As data sharing between the accelerator memories and the processor is inevitable, it is of paramount importance...

A critical issue in data replication is to wisely place data replicas which involves identifying the best possible nodes to duplicate data. Facing dynamics of data requests, this paper investigates the problem of replica placement and update in tree networks, where part of nodes have pre-existing replicas. We aim to develop efficient algorithms to...

Large-scale data center suffers from overload of data traffic on some bottleneck links, due to the fact that cloud-based services are mostly accomplished by group communications with multicast traffic. This paper investigates techniques of wireless transmission using multiple channels, instead of single available communication channel as reported i...

Programmable Systems-on-Chips (SoCs) are expected to incorporate a larger number of application-specific hardware accelerators with tightly integrated memories in order to meet stringent performance-power requirements of embedded systems. As data sharing between the accelerator memories and the processor is inevitable, it is of paramount importance...

Knowledge of the ego-vehicle's motion state is essential for assessing the collision risk in advanced driver assistance systems or autonomous driving. Vision-based methods for estimating the ego-motion of vehicle, i.e., visual odometry, face a number of challenges in uncontrolled realistic urban environments. Existing solutions fail to achieve a go...

Despite significant research efforts in pedestrian detection over the past decade, there is still a ten-fold performance gap between the state-of-the-art methods and human perception. Deep learning methods can provide good performance but suffers from high computational complexity which prohibits their deployment on affordable systems with limited...