A Simplification Heuristic For Large Flow Tables

Flow tables specifying large asynchronous sequential circuits often contain more internal states than are required to specify desired circuit behavior. Known minimization techniques appear unsuited for reduction of such large (rows X columns \u3e 250) flow tables, because of excessive computation and intermediate data requirements for problems of this size. The algorithm described here is intended to rapidly produce a simplified-but in general non-minimal-flow table. It is most economical when applied to extremely large tables and was devised primarily for automated design applications. The procedure has been programmed in PL/1 and has been incorporated into an asynchronous sequential circuit design automation system developed at the University of Missouri-Rolla. Typical flow table simplification times obtained using the program are cited. In one test re-duction of a 217 row x 8 column table to 39x8 required about 2.6 minutes (the minimum table in this case was known to be 23x8)

Synthesis heuristics for large asynchronous sequential circuits

Many well-known synthesis procedures for asynchronous sequential circuits produce minimal or near-minimal results, but are practical only for very small problems. These algorithms become unwieldy when applied to large circuits with, for example, three or more input variables and twenty or more internal states. New heuristic procedures are described which permit the synthesis of very large machines. Although the resulting designs are generally not minimal, the heuristics are able to produce near-minimal solutions orders of magnitude more rapidly than the minimal algorithms. A method for specifying sequential circuit behavior is presented. Input-output sequences define submachines or modules. When properly interconnected, these modules form the required sequential circuit. It is shown that the waveform and interconnection specifications may easily be translated into flow table form. A large flow table simplification heuristic is developed. The algorithm may be applied to tables having hundreds of rows, and handles both normal and non-normal mode circuit specifications. Nonstandard state assignment procedures for normal, fundamental mode asynchronous sequential circuits are examined. An algorithm for rapidly generating large flow table internal state assignments is proposed. The algorithms described have been programmed in PL/1 and incorporated into an automated design system for asynchronous circuits; the system also includes minimum and near-minimum variable state assignment generators, a code evaluation routine, a design equation generator, and two Boolean equation simplification procedures. Large sequential circuits designed using the system illustrate the utility of the heuristic procedures --Abstract, pages ii-iii

Constrained set-up of the tGAP structure for progressive vector data transfer

A promising approach to submit a vector map from a server to a mobile client is to send a coarse representation first, which then is incrementally refined. We consider the problem of defining a sequence of such increments for areas of different land-cover classes in a planar partition. In order to submit well-generalised datasets, we propose a method of two stages: First, we create a generalised representation from a detailed dataset, using an optimisation approach that satisfies certain cartographic constraints. Second, we define a sequence of basic merge and simplification operations that transforms the most detailed dataset gradually into the generalised dataset. The obtained sequence of gradual transformations is stored without geometrical redundancy in a structure that builds up on the previously developed tGAP (topological Generalised Area Partitioning) structure. This structure and the algorithm for intermediate levels of detail (LoD) have been implemented in an object-relational database and tested for land-cover data from the official German topographic dataset ATKIS at scale 1:50 000 to the target scale 1:250 000. Results of these tests allow us to conclude that the data at lowest LoD and at intermediate LoDs is well generalised. Applying specialised heuristics the applied optimisation method copes with large datasets; the tGAP structure allows users to efficiently query and retrieve a dataset at a specified LoD. Data are sent progressively from the server to the client: First a coarse representation is sent, which is refined until the requested LoD is reached

Traffic Engineering with Segment Routing: SDN-based Architectural Design and Open Source Implementation

Traffic Engineering (TE) in IP carrier networks is one of the functions that can benefit from the Software Defined Networking paradigm. By logically centralizing the control of the network, it is possible to "program" per-flow routing based on TE goals. Traditional per-flow routing requires a direct interaction between the SDN controller and each node that is involved in the traffic paths. Depending on the granularity and on the temporal properties of the flows, this can lead to scalability issues for the amount of routing state that needs to be maintained in core network nodes and for the required configuration traffic. On the other hand, Segment Routing (SR) is an emerging approach to routing that may simplify the route enforcement delegating all the configuration and per-flow state at the border of the network. In this work we propose an architecture that integrates the SDN paradigm with SR-based TE, for which we have provided an open source reference implementation. We have designed and implemented a simple TE/SR heuristic for flow allocation and we show and discuss experimental results.Comment: Extended version of poster paper accepted for EWSDN 2015 (version v4 - December 2015

Fast Automatic Verification of Large-Scale Systems with Lookup Tables

Modern safety-critical systems are difficult to formally verify, largely due to their large scale. In particular, the widespread use of lookup tables in embedded systems across diverse industries, such as aeronautics and automotive systems, create a critical obstacle to the scalability of formal verification. This paper presents a novel approach for the formal verification of large-scale systems with lookup tables. We use a learning-based technique to automatically learn abstractions of the lookup tables and use the abstractions to then prove the desired property. If the verification fails, we propose a falsification heuristic to search for a violation of the specification. In contrast with previous work on lookup table verification, our technique is completely automatic, making it ideal for deployment in a production environment. To our knowledge, our approach is the only technique that can automatically verify large-scale systems lookup with tables. We illustrate the effectiveness of our technique on a benchmark which cannot be handled by the commercial tool SLDV, and we demonstrate the performance improvement provided by our technique

Data-Collection for the Sloan Digital Sky Survey: a Network-Flow Heuristic

The goal of the Sloan Digital Sky Survey is ``to map in detail one-quarter of the entire sky, determining the positions and absolute brightnesses of more than 100 million celestial objects''. The survey will be performed by taking ``snapshots'' through a large telescope. Each snapshot can capture up to 600 objects from a small circle of the sky. This paper describes the design and implementation of the algorithm that is being used to determine the snapshots so as to minimize their number. The problem is NP-hard in general; the algorithm described is a heuristic, based on Lagriangian-relaxation and min-cost network flow. It gets within 5-15% of a naive lower bound, whereas using a ``uniform'' cover only gets within 25-35%.Comment: proceedings version appeared in ACM-SIAM Symposium on Discrete Algorithms (1998

Making Neural QA as Simple as Possible but not Simpler

Recent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler neural baseline systems that would justify their complexity. In this work, we propose a simple heuristic that guides the development of neural baseline systems for the extractive QA task. We find that there are two ingredients necessary for building a high-performing neural QA system: first, the awareness of question words while processing the context and second, a composition function that goes beyond simple bag-of-words modeling, such as recurrent neural networks. Our results show that FastQA, a system that meets these two requirements, can achieve very competitive performance compared with existing models. We argue that this surprising finding puts results of previous systems and the complexity of recent QA datasets into perspective