Chiplet-based systems introduce new challenges in test, debug, and repair. The complexity and number of interconnects increase the likelihood of defects, necessitating repair strategies to prevent discarding expensive systems due to a few faulty interconnects. To address these challenges, the IEEE P3405 "Standard for Chiplet Interconnect Test and Repair" is under development. This standard, originating from IEEE 1838-2019, aims to be universally applicable beyond IEEE 1838-based multi-die designs. The goal of P3405 is to standardize the test, repair, and potentially debug and diagnosis of interconnect hardware between chiplets, particularly for heterogeneous, multi-vendor integration flows. This session will present talks covering various aspects of these challenges, including the need for a language to communicate the structural and functional details of interconnects and repair options. The second presentation will focus on challenges and solutions related to clock interconnect testing and the impact of high-speed clocking. Finally, the last talk will address the testing of analog circuitry in chiplets and its application to enable high-speed communication.

While most networks have long lifetimes, temporary network infrastructure is often useful for special events, pop-up retail, or disaster response. Aninstant IoTnetwork is one that is rapidly constructed, used for a few days, then dismantled. We consider the synthesis of instant IoT networks in urban settings. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quicklyscalingto large regions while producing cost-effective solutions. We explore two qualitatively different representations of the synthesis problems using satisfiability modulo convex optimization (SMC), and mixed-integer linear programming (MILP). The former is more expressive, for our problem, than the latter, but is less well-suited for solving optimization problems like ours. We show how to express our network synthesis in these frameworks. To scale to problem sizes beyond what these frameworks are capable of, we develop ahierarchical synthesistechnique that independently synthesizes networks in sub-regions of the deployment area, then combines these. We find that, while MILP outperforms SMC in some settings for smaller problem sizes, the fact that SMC's expressivity matches our problem ensures that it uniformly generates better quality solutions at larger problem sizes.

Public outdoor surveillance cameras often have limited metadata describing their properties. Frequently, a public camera’s precise position, orientation, focal length, and image center are unknown; these attributes are necessary to precisely pinpoint the location of events seen in the camera. In this article, we ask: what is the minimal information needed to accurately estimate these properties for public cameras? We show, using a judicious combination of projective geometry, neural networks, and crowd-sourced annotations from human workers, that it is possible to, for example, localize 95% of the cameras in our test data set to within 12 m using a single image taken from the camera. This performance is an order of magnitude better than PoseNet, a state-of-the-art neural network that needs significantly more information than our approach, and can only estimate position and orientation (and not other properties). Finally, we show that the camera’s inferred pose and properties can help design a number of virtual sensors , all of which have good accuracy.

Advances in optimization and constraint satisfaction techniques, together with the availability of elastic computing resources, have spurred interest in large-scale network verification and synthesis. Motivated by this, we consider the top-down synthesis of ad-hoc IoT networks for disaster response and search and rescue operations. This synthesis problem must satisfy complex and competing constraints: sensor coverage, line-of-sight visibility, and network connectivity. The central challenge in our synthesis problem is quickly scaling to large regions while producing cost-effective solutions. We explore a representation of the synthesis problems using a novel constraint satisfaction paradigm, satisfiability modulo convex optimization (SMC). We choose SMC because it matches the expressivity needs for our network synthesis. To scale to large problem sizes, we develop a hierarchical synthesis technique that independently synthesizes networks in sub-regions of the deployment area, then combines these. Our experiments show that SMC consistently generates better quality solutions than a baseline synthesis approach based on Mixed Integer Linear Programming (MILP).

Persistent surveillance with aerial vehicles (drones) subject to connectivity and power constraints is a relatively uncharted domain of research. To reduce the complexity of multi-drone motion planning, most state-of-the-art solutions ignore network connectivity and assume unlimited battery power. Motivated by this and advances in optimization and constraint satisfaction techniques, we introduce a new persistent surveillance motion planning problem for multiple drones that incorporates connectivity and power consumption constraints. We use a recently developed constrained optimization tool (Satisfiability Modulo Convex Optimization (SMC)) that has the expressivity needed for this problem. We show how to express the new persistent surveillance problem in the SMC framework. Our analysis of the formulation based on a set of simulation experiments illustrates that we can generate the desired motion planning solution within a couple of minutes for small teams of drones (up to 5) confined to a 7 × 7 × 1 grid-space.

The field of IoT has blossomed and is positively influencing many application domains. In this article, we bring out the unique challenges this field poses to research in computer systems and networking. The unique challenges arise from the unique characteristics of IoT systems such as the diversity of application domains where they are used and the increasingly demanding protocols they are being called upon to run (such as video and LIDAR processing) on constrained resources (on-node and network). We show how these open challenges can benefit from foundations laid in other areas, such as fifth-generation network cellular protocols, machine learning model reduction, and device-edge-cloud offloading. We then discuss the unique challenges for reliability, security, and privacy posed by IoT systems due to their salient characteristics which include heterogeneity of devices and protocols, dependence on the physical environment, and the close coupling with humans. We again show how open research challenges benefit from the reliability, security, and privacy advancements in other areas. We conclude by providing a vision for a desirable end state for IoT systems.

Detecting activities from video taken with a single camera is an active research area for ML-based machine vision. In this paper, we examine the next research frontier: near real-time detection of complex activities spanning multiple (possibly wireless) cameras, a capability applicable to surveillance tasks. We argue that a system for such complex activity detection must employ a hybrid design: one in which rule-based activity detection must complement neural network based detection. Moreover, to be practical, such a system must scale well to multiple cameras and have low end-to-end latency. Caesar, our edge computing based system for complex activity detection, provides an extensible vocabulary of activities to allow users to specify complex actions in terms of spatial and temporal relationships between actors, objects, and activities. Caesar converts these specifications to graphs, efficiently monitors camera feeds, partitions processing between cameras and the edge cluster, retrieves minimal information from cameras, carefully schedules neural network invocation, and efficiently matches specification graphs to the underlying data in order to detect complex activities. Our evaluations show that Caesar can reduce wireless bandwidth, on-board camera memory, and detection latency by an order of magnitude while achieving good precision and recall for all complex activities on a public multi-camera dataset.

We present the Intelligent Robotic IoT System (IRIS), a modular, portable, scalable, and open-source testbed for robotic wireless network research. There are two key features that separate IRIS from most of the state-of-the-art multi-robot testbeds. (1)Portability: IRIS does not require a costly static global positioning system such as a VICON system nor time-intensive vision-based SLAM for its operation. Designed with an inexpensive Time Difference of Arrival (TDoA)localization system with centimeter level accuracy, the IRIS testbed can be deployed in an arbitrary uncontrolled environment in a matter of minutes. (2)Programmable Wireless Communication Stack: IRIS comes with a modular programmable low-power IEEE 802.15.4 radio and IPv6 network stack on each node. For the ease of administrative control and communication, we also developed a lightweight publish-subscribe overlay protocol called ROMANO that is used for bootstrapping the robots (also referred to as the IRISbots), collecting statistics, and direct control of individual robots, if needed. We detail the modular architecture of the IRIS testbed design along with the system implementation details and localization performance statistics.

We present two novel distributed algorithms for hole detection in a wireless sensor network (WSN) based on the distributed Delaunay triangulation of the underlying communication graph. The first, which we refer to as the distance-vector hole determination (DVHD) algorithm, is based on traditional distance vector routing for multi-hop networks and shortest path lengths between node pairs. The second, which we refer to as the Gaussian curvature-based hole determination (GCHD) algorithm, applies the Gauss-Bonnet theorem on the Delaunay graph to calculate the number of holes based on the graph's Gaussian curvature. We present a detailed comparative performance analysis of both methods based on simulations, showing that while DVHD is conceptually simpler, the GCHD algorithm shows better performance with respect to run-time and message count per node.

This paper proposes an improved and adaptive variant of the differential evolution algorithm for the design of two-channel quadrature mirror filters with linear phase characteristics. To match the ideal system response characteristics, the algorithm is employed to optimize the values of the filter bank coefficients. The filter response is optimized in both passband and stopband. The overall filter bank response aims at minimizing objectives like reconstruction error, mean square error in passband, and mean square error in stopband. Effective designing can be achieved by efficiently minimizing the objective function. The proposed algorithm is able to perform better than the other existing design methods. Five different design examples are presented to validate the effectiveness of the proposed approach over other conventional design techniques, as well as state-of-the-art evolutionary algorithms found in the literature.

Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. In this article, we introduce a new variant of PSO referred to as Hierarchical D-LPSO (Dynamic Local Neighborhood based Particle Swarm Optimization). In this new variant of PSO the particles are arranged following a dynamic hierarchy. Within each hierarchy the particles search for better solution using dynamically varying sub-swarms i.e. these sub-swarms are regrouped frequently and information is exchanged among them. Whether a particle will move up or down the hierarchy depends on the quality of its so-far best found result. The swarm is largely influenced by the good particles that move up in the hierarchy. The performance of Hierarchical D-LPSO is tested on the set of 25 numerical benchmark functions taken from the competition and special session on real parameter optimization held under IEEE Congress on Evolutionary Computation (CEC) 2005. The results have been compared to those obtained with a few best-known variants of PSO as well as a few significant existing evolutionary algorithms.

In recent years, there has been a growing interest in real world application of heuristic methods. Memetic Algorithm (MA) is one of such effective heuristics. In this paper, we represent an efficient MA for determining optimal proportional-integral-derivative (PID) controller parameters of an AVR system. This MA is developed by combining a competitive variant of Deferential Evolution (DE) and a Local Search method. The proposed method has excellent features, such as easy implementation, stable convergence characteristic and good computational efficiency. Fast tuning of PID controller parameters results in far better performance of the controller. Performance of our proposed algorithm is compared with other famous heuristics and the simulation results clearly indicate that our proposed approach is indeed more efficient and robust in improving the step response of an AVR system.

The success of system test is measured by test quality and cost. System test quality and cost rely on several factors, such as component and board test quality, system test completeness, the support of system diagnostics, and a process that controls overall quality, resource and cost balances. Traditional structural test techniques used at the component level can achieve both high test quality and low test costs. This paper describes an approach to extend the functionalities of structural test techniques to the board and system level to improve the test accessibility, test time, and diagnostic capability. This approach has become practice in a large telecommunication company and the benefits received from this practice are tremendous. Examples will be given at the end of the paper

While the IEEE P1500 standards working group is on the verge of recommending a standard test interface for non-mergeable cores, a need was felt to adopt a standard methodology to achieve easy test interoperability for non-merged core (RTL, gate level) integration. A task force was formed under the P1500 working group investigating this issue and came up with a recommendation. DFT Disclosure Document (DDD) was the outcome of this exercise. The DDD, targeted to be an IEEE standard recommended practice document, aims at capturing the DFT related experience the core provider would like to share with the integrator to facilitate easy test integration. This paper presents the objective and structure of this document along with some use models from participating companies as well as an example.