Design and Evaluation of a Tunable PUF Architecture for FPGAs

FPGA-based Physical Unclonable Functions (PUF) have emerged as a viable alternative to permanent key storage by turning effects of inaccuracies during the manufacturing process of a chip into a unique, FPGA-intrinsic secret. However, many fixed PUF designs may suffer from unsatisfactory statistical properties in terms of uniqueness, uniformity, and robustness. Moreover, a PUF signature may alter over time due to aging or changing operating conditions, rendering a PUF insecure in the worst case. As a remedy, we propose CHOICE , a novel class of FPGA-based PUF designs with tunable uniqueness and reliability characteristics. By the use of addressable shift registers available on an FPGA, we show that a wide configuration space for adjusting a device-specific PUF response is obtained without any sacrifice of randomness. In particular, we demonstrate the concept of address-tunable propagation delays, whereby we are able to increase or decrease the probability of obtaining “ 1 ”s in the PUF response. Experimental evaluations on a group of six 28 nm Xilinx Artix-7 FPGAs show that CHOICE PUFs provide a large range of configurations to allow a fine-tuning to an average uniqueness between 49% and 51%, while simultaneously achieving bit error rates below 1.5%, thus outperforming state-of-the-art PUF designs. Moreover, with only a single FPGA slice per PUF bit, CHOICE is one of the smallest PUF designs currently available for FPGAs. It is well-known that signal propagation delays are affected by temperature, as the operating temperature impacts the internal currents of transistors that ultimately make up the circuit. We therefore comprehensively investigate how temperature variations affect the PUF response and demonstrate how the tunability of CHOICE enables us to determine configurations that show a high robustness to such variations. As a case study, we present a cryptographic key generation scheme based on CHOICE PUF responses as device-intrinsic secret and investigate the design objectives resource costs, performance, and temperature robustness to show the practicability of our approach.

Towards Optimal Parallelism-Aware Service Chaining and Embedding

<div>Emerging 5G technologies can significantly reduce end-to-end service latency for applications requiring strict quality of service (QoS). With network function virtualization (NFV), to complete a client’s request from those applications, the client’s data can sequentially go through multiple service functions (SFs) for processing/analysis but introduce additional processing delay. To reduce the processing delay from the serially-running SFs, network function parallelism (NFP) that allows multiple SFs to run in parallel is introduced. In this work, we study how to apply NFP into the SF chaining and embedding process such that the latency, including processing and propagation delays, can be jointly minimized. We introduce a novel augmented graph to address the parallel relationship constraint among the required SFs. Considering parallel relationship constraints, we propose a novel problem called parallelism-aware service function chaining and embedding (PSFCE). For this problem, we propose a near-optimal maximum parallel block gain (MPBG) first optimization algorithm when computing resources at each physical node are enough to host the required SFs. When computing resources are limited, we propose a logarithm-approximate algorithm, called parallelism-aware SFs deployment (PSFD), to jointly optimize processing and propagation delays. We conduct extensive simulations on multiple network scenarios to evaluate the performances of our schemes. Accordingly, we find that (i) MPBG is near-optimal, (ii) the optimization of end-to-end service latency largely depends on the processing delay in small networks and is impacted more by the propagation delay in large networks, and (iii) PSFD outperforms the schemes directly extended from existing works regarding end-to-end latency.</div>

Towards Optimal Parallelism-Aware Service Chaining and Embedding

<div>Emerging 5G technologies can significantly reduce end-to-end service latency for applications requiring strict quality of service (QoS). With network function virtualization (NFV), to complete a client’s request from those applications, the client’s data can sequentially go through multiple service functions (SFs) for processing/analysis but introduce additional processing delay. To reduce the processing delay from the serially-running SFs, network function parallelism (NFP) that allows multiple SFs to run in parallel is introduced. In this work, we study how to apply NFP into the SF chaining and embedding process such that the latency, including processing and propagation delays, can be jointly minimized. We introduce a novel augmented graph to address the parallel relationship constraint among the required SFs. Considering parallel relationship constraints, we propose a novel problem called parallelism-aware service function chaining and embedding (PSFCE). For this problem, we propose a near-optimal maximum parallel block gain (MPBG) first optimization algorithm when computing resources at each physical node are enough to host the required SFs. When computing resources are limited, we propose a logarithm-approximate algorithm, called parallelism-aware SFs deployment (PSFD), to jointly optimize processing and propagation delays. We conduct extensive simulations on multiple network scenarios to evaluate the performances of our schemes. Accordingly, we find that (i) MPBG is near-optimal, (ii) the optimization of end-to-end service latency largely depends on the processing delay in small networks and is impacted more by the propagation delay in large networks, and (iii) PSFD outperforms the schemes directly extended from existing works regarding end-to-end latency.</div>

Autonomous extraction of millimeter-scale deformation in InSAR time series using deep learning

Systematically characterizing slip behaviours on active faults is key to unraveling the physics of tectonic faulting and the interplay between slow and fast earthquakes. Interferometric Synthetic Aperture Radar (InSAR), by enabling measurement of ground deformation at a global scale every few days, may hold the key to those interactions. However, atmospheric propagation delays often exceed ground deformation of interest despite state-of-the art processing, and thus InSAR analysis requires expert interpretation and a priori knowledge of fault systems, precluding global investigations of deformation dynamics. Here, we show that a deep auto-encoder architecture tailored to untangle ground deformation from noise in InSAR time series autonomously extracts deformation signals, without prior knowledge of a fault’s location or slip behaviour. Applied to InSAR data over the North Anatolian Fault, our method reaches 2 mm detection, revealing a slow earthquake twice as extensive as previously recognized. We further explore the generalization of our approach to inflation/deflation-induced deformation, applying the same methodology to the geothermal field of Coso, California.

When latency matters

Several emerging classes of interactive applications are demanding for extremely low-latency to be fully unleashed, with edge computing generally regarded as a key enabler thanks to reduced delays. This paper presents the outcome of a large-scale end-to-end measurement campaign focusing on task-offloading scenarios, showing that moving the computation closer to the end-users, alone, may turn out not to be enough. Indeed, the complexity associated with modern networks, both at the access and in the core, the behavior of the protocols at different levels of the stack, as well as the orchestration platforms used in data-centers hide a set of pitfalls potentially reverting the benefits introduced by low propagation delays. In short, we highlight how ensuring good QoS to latency-sensitive applications is definitely a multi-dimensional problem, requiring to cope with a great deal of customization and cooperation to get the best from the underlying network.

Achieving maximum flow for Inter-planetary networks

Inter-planetary network (IPN) systems on earth and spacecrafts communicate with systems on other planets. Communications and data are often relayed and delivered by the IPN systems. Delay/Disruption Tolerant Networking (DTN) have several unique characteristic features with significant challenges such as intermittent link connectivity, long and variable propagation delays, link bandwidth asymmetry, high link error rates and Power constraints. It is vital to overcome these challenges to achieve maximum flow for IPN systems. The aim of this project is to address the issue of achieving maximum flow in Inter-planetary network. To tackle these issues, a DTN model as time aggregated graph (TAG) was proposed and a bidirectional storage transfer series was used to correlate various time intervals and aid in maximizing flow. Our results suggest that the maximum flow can be achieved by seeking an augmenting path, computing its maximum flow, and getting its residual network using an iterative approach. In future, we intend to investigate the increase in difficulty of achieving maximum flow.

Interface Trap Charge Effects of Monolithic 3D Junctionless Field-Effect Transistors (JLFET) Inverter

We investigated the effect of the interface trap charge in a monolithic three-dimensional inverter structure composing of JLFETs (M3DINV-JLFET), using the interface trap charge distribution extracted in the previous study. The effect of interface trap charge was compared with a conventional M3DINV composing of MOSFETs (M3DINV-MOSFETs) by technology computer-aided design simulation. When the interface trap charges in both M3DINV-JLFET and M3DINV-MOSFET are added, the threshold voltages, on-current levels, and subthreshold swings of both JLFETs and MOSFETs increase, decrease, and increase, respectively, and switching voltages and propagation delays of M3DINV are shifted and increased, respectively. However, since JLFET and MOSFET have different current paths of bulk and interface in channel, respectively, MOSFET is more affected by the interface trap, and M3DINV-JLFET has almost less effect of interface trap at different thickness of interlayer dielectric, compared to M3DINV-MOSFET.

A Cognitive Anycast Routing Method for Delay-Tolerant Networks

A cognitive networking approach to the anycast routing problem for delay-tolerant networking (DTN) is proposed. The method is suitable for the space–ground and other domains where communications are recurrently challenged by diverse link impairments, including long propagation delays, communication asymmetry, and lengthy disruptions. The proposed method delivers data bundles achieving low delays by avoiding, whenever possible, link congestion and long wait times for contacts to become active, and without the need of duplicating data bundles. Network gateways use a spiking neural network (SNN) to decide the optimal outbound link for each bundle. The SNN is regularly updated to reflect the expected cost of the routing decisions, which helps to fine-tune future decisions. The method is decentralized and selects both the anycast group member to be used as the sink and the path to reach that node. A series of experiments were carried out on a network testbed to evaluate the method. The results demonstrate its performance advantage over unicast routing, as anycast routing is not yet supported by the current DTN standard (Contact Graph Routing). The proposed approach yields improved performance for space applications that require as-fast-as-possible data returns.

Dynamic Behavior for a Coupled Nonlinear Oscillator Model with Distributed and Discrete Delays

— In this paper, the oscillatory behavior of the solutions for a coupled nonlinear oscillator model with distributed and discrete delays is investigated. Time delay induced partial death patterns with conjugate coupling in relay oscillators has been investigated in the literature. According to the practical problem, the propagation delays are not only the discrete delays, but also with distributed delay. A model includes distributed and discrete delays is considered. By mathematical analysis method, the oscillatory behavior of the coupled nonlinear oscillator model is brought to the instability of the uniqueness equilibrium point and the boundedness of the solutions. Some sufficient conditions are provided to guarantee the oscillation of the solutions. Computer simulations are given to support the present results. Our simulation suggests that the two theorems are only sufficient conditions.

Cardinal Metrics in Blockchain Functioning

Blockchain technology has been acquiring pace in deployments and implementations across globe vide association with large number of domains apart from widely known finance domain. These deployments are variegated in designs, have various architectures and possess functional differences. The commonality exists in deriving the benefits of blockchain technology through various technical variants of the widely known bitcoin blockchain architecture. Though still in evolving stage, the blockchain technology has been able to make an absolute mark in the industries, corporate and governance mechanisms to affirm that it’s part of a definite future. With devices estimate up to 50 billion in ecosystem of Internet-of-Things by 2025, the blockchain technology is soon going to be an integral part of future smart world. The deployment of any blockchain architecture might be able to accomplish the functional requirements as per design but the measurement of desired blockchain performance persists on a lot of parameters which need a balance and fine tuning established on purpose it has been designed for. In current times, transaction commit delays are being observed in bitcoin ecosystem. This paper identifies parameter effects on a bitcoin blockchain and measures the performance vide a bitcoin simulator effecting into tuning parameters like block size, blocks and number of nodes to analyze performance. The tuning effects into blockchain performance has been quantified, analyzed and discussed with focus on measuring and reducing the transaction propagation delays in a bitcoin environment. The paper concludes with heat map modeling plotted on Jupyter notebook with datasets derived.