ILP-based cost-optimal DSP synthesis with module selection and data format conversion

Solving Integer Linear Programming (ILP) models generally lies in the category of NP-hard problems. Therefore, as the size of ILP models grows, the efficiency of exact algorithms for solving the models reduced significantly and for large models it is not possible to have the result. Genetic Algorithm (GA) is a metaheuristic method capable of adjusting and redesigning parameters and operations according to the characteristics of ILP models. Still GA has huge search space for large models and parallelization is a suitable technique to tackle this problem. This paper presents a scalable parallel GA to solve large ILP models derived from behavioral synthesis of digital circuits. We show that although models have non-binary variables, only binary variables are sufficient for coding chromosomes. We also use ”unknown” values for some genes to decrease the likelihood of inconsistency in the encoded constraints. Our experiments verify the efficiency and scalability of the proposed algorithm on multicore platforms. The proposed method outperforms IBM ILOG CPLEX 12.6 and MI-LXPM algorithm where the ILP models include 550 to 2258 int / binary decision variables. Also, the results indicate that the saturation point of using parallel processing elements for solving the large ILP models is at least 60.

Integer Linear Programming (ILP) is commonly used in high level and system level synthesis. It is an NP-Complete problem (in general cases). There exists some tools that give an optimal solution for small ILP formulations. Nevertheless, these tools may not give solutions for complex formulations. In this paper, we present a solution to overcome the problem of complexity in ILP formulations. We propose a partitioning methodology based on a constraint graph representing all the constraints included in any ILP formulation. To direct the partitioning, the constraint graph nodes are grouped to represent Data Flow Graph (DFG) nodes. This reduced constraint graph can be used to partition any ILP formulation based on DFG. We illustrate this method on ILP formulation for scheduling. Results on ILP scheduling formulations are promising.

This paper describes the demonstration of a simple all-optical data format conversion scheme between return-to-zero (RZ) and nonreturn-to-zero (NRZ) that employs a semiconductor optical amplifier (SOA) in a nonlinear optical loop mirror. The format conversion has been performed between the most widely used data formats-NRZ and RZ formats. The format conversion scheme is based on gain variation by an intensity-dependent phase change in an SOA-loop mirror. The input data stream acts as a control signal that induces the phase differences between clockwise- and counterclockwise-propagating data inside an SOA-loop mirror. It is possible to change the data format of the output data stream by controlling the phase differences of the clockwise and counterclockwise pulse in an SOA-loop mirror appropriately. For the converted NRZ data from RZ data, 10-Gb/s error-free transmission up to 78 km over standard single-mode fiber has been obtained. By comparing the conventional NRZ transmission with the Mach-Zehnder modulation scheme, the proposed RZ-to-NRZ conversion shows an improved transmission performance. The NRZ-to-RZ conversion has clear eye openings up to 78 km. On the contrary, the conventional RZ binary data from a mode-locked laser has a nearly closed eye even at 52 km. The converted RZ data has a 2-dB conversion power margin to the injected NRZ data, which indicates an increase in the receiver sensitivity due to the signal format conversion. The improved transmission distance of the converted RZ signal is due to the duobinary coding effect of the SOA-loop mirror. The SOA has the possibility of high-speed operation over 40 Gb/s, and the SOA-loop mirror has the capabilities of format and wavelength conversions. Therefore, the SOA-loop mirror can be a universal building block in future all-optical networks. In addition, the proposed format conversion scheme can serve as an important format converter between the ultrafast optical-time-division-multiplexed networks and the lower line-rate wavelength-division-multiplexed networks.

In this paper, we propose an integer linear programming (ILP) model for static elevator operation problems. The proposed model is based on hall- and car-calls which correspond to pickup and delivery requests in pickup-and-delivery problems (PDPs), thus is similar to ILP models for PDPs. The primary difference between those models and the proposed model is the prevention of the reverse run, which means that an elevator with a passenger is moved to the direction opposite to that of the passenger. In computer illustrations, the proposed model is roughly investigated by being compared with a trip-based ILP model which has been proposed by the authors.

An Optimal Cross-Layer Scheduling for Periodic WSN Applications

This paper focus on the routing, modulation level and spectrum allocation (RMLSA) problem in the next generation datacentre networks, which own the advantages of both Elastic Optical Networks (EON) and Software Defined Networking (SDN). We proposes an Integer Linear Programming (ILP) model for this problem. Simulation results show the ILP model achieves an outstanding load balancing performance.

This paper addresses the problem of minimizing spectrum usage for shared-path protection with joint failure probability constraint in flexible bandwidth optical networks. To achieve this goal, we propose an integer linear programming (ILP) model for shared-path protection and a heuristic spectrum-aware shared protection (SASP) algorithm that considers joint failure probability. Simulation results show that the ILP model minimizes the total usage of frequency slots and average hops, but leads to high average joint failure probability. Moreover, the SASP algorithm achieves better trade-off between total usage of frequency slots and average joint failure probability compared to the ILP model and a conventional shared-path protection (CSPP) algorithm. As expected, in a 14-nodes network, the SASP algorithm performs better with respect to total spectrum usage and average hops, but results in much larger average joint failure probability compared to the CSPP algorithm.

Production Lot Sizes on a Single Imperfect Machine: FPTAS vs ILP models

In this work, we propose an integer linear programming (ILP) model to schedule a set of weakly-hard real-time tasks on a heterogeneous multiprocessor system to minimize energy consumption with the predictable performance. The ILP model provides an optimal solution in terms of energy consumption by selecting the appropriate frequency for each task with (m,k) weakly-hard deadline constraint.

Efficient and scalable provisioning of always-on multicast streaming services

This paper presents a new heuristic, concurrent, iterative loop-based scheduling and allocation algorithm for high-level synthesis of digital signal processing (DSP) architectures using heterogeneous functional units. In a heterogeneous architecture, functional units could be either bit-serial or digit-serial or bit-parallel. We assume that a library of functional units based on heterogeneous implementation style is available. Experiments show that this new heuristic synthesis approach generates optimal and near-optimal area solutions. Although optimum synthesis of such architectures were proposed recently using an integer linear programming (ILP) model, our method can produce similar solutions in one to two orders of magnitude less time, at the expense of sacrificing the cost optimality. We compare the solutions generated by the proposed algorithm with the optimal solutions generated by the ILP approach and other recent techniques. We have incorporated this new algorithm into the Minnesota ARchitecture Synthesis (MARS-II) system.

The work presented in this paper focuses on behavioral level power optimization. Specifically, we address the problem of scheduling a data-flow graph under latency constraints. We have developed an integer linear program (ILP) model and a Modified Force-Directed Scheduling (MFDS) that minimize the peak power while satisfying timing constraints. Our integer linear programming method extends the traditional ILP approach that minimizes resources to include peak power consideration while adding extensions for multi-cycle and pipelined arithmetic components. In our benchmark results, the peak power is reduced after scheduling based on ILP method and MFDS algorithm and is reduced significantly after scheduling and pipelining are both applied. The results obtained by the heuristic-based algorithms (MFDS) match very well with those obtained by the integer linear programming (ILP) methods. While the results obtained by heuristic-based algorithm are approximate, the results obtained by the integer linear programming methods are optimal. But the heuristic-based algorithm is faster than the integer linear programming methods.

A high-level built-in self-test (BIST) synthesis involves several tasks such as system register assignment, interconnection assignment, and BIST register assignment. Existing high-level BIST synthesis methods perform the tasks sequentially at the cost of global optimality. We proposed a new approach based on an integer linear programming (ILP). Our method achieves optimal solutions for most circuits in hardware overhead, but it takes a long processing time. In this paper, we present a heuristic method to address this problem. The heuristic partitions a given data flow graph into smaller regions based on control steps and applies the ILP for each region successively. Our heuristic reduces the processing time by several orders of magnitude, while the quality of the solution is slightly compromised. We present experimental results for six circuits and compare the results with other BIST synthesis methods.

We present new integer linear programming (ILP) models for NP-hard optimisation problems in instances of the Stable Marriage problem with Ties and Incomplete lists (SMTI) and its many-to-one generalisation, the Hospitals/Residents problem with Ties (HRT). These models can be used to efficiently solve these optimisation problems when applied to (i) instances derived from real-world applications, and (ii) larger instances that are randomly-generated. In the case of SMTI, we consider instances arising from the pairing of children with adoptive families, where preferences are obtained from a quality measure of each possible pairing of child to family. In this case, we seek a maximum weight stable matching. We present new algorithms for preprocessing instances of SMTI with ties on both sides, as well as new ILP models. Algorithms based on existing state-of-the-art models only solve 6 of our 22 real-world instances within an hour per instance, and our new models incorporating dummy variables and constraint merging, together with preprocessing and a warm start, solve all 22 instances within a mean runtime of a minute. For HRT, we consider instances derived from the problem of assigning junior doctors to foundation posts in Scottish hospitals. Here, we seek a maximum size stable matching. We show how to extend our models for SMTI to HRT and reduce the average running time for real-world HRT instances by two orders of magnitude. We also show that our models outperform by a wide margin all known state-of-the-art models on larger randomly-generated instances of SMTI and HRT.

Nowadays, large-scale failures in elastic optical inter-data center networks (EO-DCNs) are critical issues for network survivability. Once the original connection or the primary DC fails, an alternative data center (DC) can serve the cloud services with replicated content. In this paper, we study disaster-resilient cloud services provisioning with distance-adaptive modulation formats, considering content placement, routing with modulation format adaption, protection of path and content, and spectrum allocation simultaneously. Both dedicated end-to-content backup path protection (DEBPP) and shared end-to-content backup path protection (SEBPP) are studied. First, integer linear program (ILP) models are formulated with the objective of minimum spectrum usage. For the sake of scalability, a column generation (CG) approach is developed to decompose the ILP model into a master problem and a pricing problem. The CG approach is shown to achieve comparable results to ILP models. Numerical simulations are conducted to investigate the spectrum usage of DEBPP and SEBPP with different traffic parameters and different network topologies.