Samitha Samaranayake

A reconfigurable shared scan-in architecture

In this paper, an efficient technique for test data volume reduction based on the shared scan-in (Illinois Scan) architecture and the scan chain reconfiguration (Dynamic Scan) architecture is defined. The composite architecture is created with analysis that relies on the compatibility relation of scan chains. Topological analysis and compatibility analysis are used to maximize gains in test data volume and test application time. The goal of the proposed synthesis procedure is to test all detectable faults in broadcast test mode using minimum scan-chain configurations. As a result, more aggressive sharing of scan inputs can be applied for test data volume and test application time reduction. The experimental results demonstrate the efficiency of the proposed architecture for real-industrial circuits.

Changing the scan enable during shift

This paper extends the reconfigurable shared scan-in architecture (RSSA) to provide additional ability to change values on the scan configuration signals (scan enable signals) during the scan operation on a per-shift basis. We show that the extra flexibility of reconfiguring the scan chains every shift cycle reduces the number of different configurations required by RSSA while keeping test coverage the same. In addition a simpler analysis can be used to construct the scan chains. This is the first paper of its kind that treats the scan enable signal as a test data signal during the scan operation of a test pattern. Results are presented on some ISCAS as well as industrial circuits.

Dynamic scan: driving down the cost of test

Two factors primarily drive the soaring cost of semiconductor test: the number of test patterns applied to each chip and the time it takes to run each pattern. Typical semiconductor testing for each chip involves a set of 1,000 to 5,000 test patterns. These tests are applied through scan chains that operate at about 25 MHz. Depending on the size of the scan chains on the chip, a set of test patterns can take a few seconds to execute per chip. Its easy to see that even a small decrease in either the number of patterns or the time to execute them can quickly add up to big savings across millions of fabricated chips. This potential savings forms the basis for dynamic scan, a new approach to the well-established scan test methodology. The authors initial studies indicate that dynamic scan could easily reduce the time spent applying test patterns by 40 percent. A more theoretical analysis shows a potential savings of as much as 80 percent.

A PDE-ODE Model for a Junction with Ramp Buffer

We consider the Lighthill--Whitham--Richards traffic flow model on a junction composed by one mainline, an onramp, and an offramp, which are connected by a node. The onramp dynamics is modeled using an ordinary differential equation describing the evolution of the queue length. The definition of the solution of the Riemann problem at the junction is based on an optimization problem and the use of a right-of-way parameter. The numerical approximation is carried out using a Godunov scheme, modified to take into account the effects of the onramp buffer. We present the result of some simulations and numerically check the convergence of the method.

Adjoint-Based Optimization on a Network of Discretized Scalar Conservation Laws with Applications to Coordinated Ramp Metering

The adjoint method provides a computationally efficient means of calculating the gradient for applications in constrained optimization. In this article, we consider a network of scalar conservation laws with general topology, whose behavior is modified by a set of control parameters in order to minimize a given objective function. After discretizing the corresponding partial differential equation models via the Godunov scheme, we detail the computation of the gradient of the discretized system with respect to the control parameters and show that the complexity of its computation scales linearly with the number of discrete state variables for networks of small vertex degree. The method is applied to the problem of coordinated ramp metering on freeway networks. Numerical simulations on the I15 freeway in California demonstrate an improvement in performance and running time compared with existing methods. In the context of model predictive control, the algorithm is shown to be robust to noise in the initial data and boundary conditions.

Speedup Techniques for the Stochastic on-time Arrival Problem

We consider the stochastic on-time arrival (SOTA) routing problem of finding a routing policy that maximizes the probability of reaching a given destination within a pre-specified time budget in a road network with probabilistic link travel-times. The goal of this work is to provide a theoretical understanding of the SOTA problem and present ecient computational techniques to enable the development of practical applications for stochastic routing. We present multiple speedup techniques that include a label-setting algorithm based on the existence of a minimal link travel-time on each road link, advanced convolution methods centered on the Fast Fourier Transform and the idea of zero-delay convolution, and localization techniques for determining an optimal order of policy computation. We describe the algorithms for each speedup technique and analyze their impact on computation time. We also analyze the behavior of the algorithms as a function of the network topology and present numerical results to demonstrate this. Finally, experimental results are provided for the San Francisco Bay Area arterial road network to show how the algorithms would work in an operational setting. 1998 ACM Subject Classification F.2.0 General

An adaptive routing system for location-aware mobile devices on the road network

As congestion problems become a greater concern and negatively impact society, solutions which alleviate them are needed to improve the performance of the transportation system. Routing systems which take into account the travel-time experienced by the driver have been largely unexplored in the domain of adaptive routing. In this article, we present a system which enables users of smartphones to obtain directions generated using an algorithm which provides an optimal routing policy for reliable on-time arrival; that is, directions which seek to maximize the probability of arriving to the destination within a given time budget, rather than to minimize the travel time based on posted speed limits. Our work leverages the geolocation capabilities of smartphones to provide optimal routing directions along the route dependent on the realized (experienced) travel time. The adaptive routing scheme we implement allows for significant power savings and improved driver safety compared to classical routing algorithms; special attention is paid to minimizing driver distraction by emphasizing aural and graphical components over textual elements during route guidance. Finally, we illustrate system performance and design choices on synthetic examples and real traffic data from the Mobile Millennium system in San Francisco.

Learning the dependency structure of highway networks for traffic forecast

Forecasting road traffic conditions requires an accurate knowledge of the spatio-temporal dependencies of traffic flow in transportation networks. In this article, a Bayesian network framework is introduced to model the correlation structure of highway networks in the context of traffic forecast. We formulate the dependency learning problem as an optimization problem and propose an efficient algorithm to identify the inclusion-optimal dependency structure of the network given historical observations. The optimal dependency structure learned by the proposed algorithm is evaluated on benchmark tests to show its robustness to measurement uncertainties and on field data from the Mobile Millennium traffic estimation system to show its applicability in an operational setting.

Discrete-time system optimal dynamic traffic assignment (SO-DTA) with partial control for horizontal queuing networks

We consider the System Optimal Dynamic Traffic Assignment problem with Partial Control (SO-DTA-PC) for general networks with horizontal queuing. The goal of which is to optimally control any subset of the networks agents to minimize the total congestion of all agents in the network. We adopt a flow dynamics model that is a Godunov discretization of the Lighthill-Williams-Richards (LWR) partial differential equation with a triangular flux function and a corresponding multi-commodity junction solver. Full Lagrangian paths are assumed to be known for the controllable agents, while we only assume knowledge of the aggregate split ratios for the non-controllable (selfish) agents. We solve the resulting finite horizon non-linear optimal control problem using the discrete adjoint method.

Vehicle routing for shared-mobility systems with time-varying demand

This work considers mobility systems in which a shared fleet of self-driving vehicles is used to transport passengers. More specifically, we focus on policies to route both passenger-filled and empty vehicles when the travel demand is time-varying. In this setting, we argue that metrics, such as the cost to relocate empty vehicles, which are well-defined in a stand-alone capacity under steady-state conditions, now make sense only within a framework that reflects inherent tradeoffs with other metrics, e.g., the fleet size and the quality of service provided. As a first step toward developing a general theory of time-varying, shared-mobility systems, we provide an optimization framework that models passengers and vehicles as continuous fluids, and their movement as fluid flows. The model is used to develop some initial performance results related to the minimum number of vehicles required to avoid passenger queueing. Finally, simulation results of a hypothetical shared mobility system based in Singapore demonstrate how a fleet manager could use our optimization approach to select a vehicle routing policy.