Automated Circuit Approximation Method Driven by Data Distribution

We propose an application-tailored data-driven fully automated method for functional approximation of combinational circuits. We demonstrate how an application-level error metric such as the classification accuracy can be translated to a component-level error metric needed for an efficient and fast search in the space of approximate low-level components that are used in the application. This is possible by employing a weighted mean error distance (WMED) metric for steering the circuit approximation process which is conducted by means of genetic programming. WMED introduces a set of weights (calculated from the data distribution measured on a selected signal in a given application) determining the importance of each input vector for the approximation process. The method is evaluated using synthetic benchmarks and application-specific approximate MAC (multiply-and-accumulate) units that are designed to provide the best trade-offs between the classification accuracy and power consumption of two image classifiers based on neural networks.Comment: Accepted for publication at Design, Automation and Test in Europe (DATE 2019). Florence, Ital

Logical topology design for IP rerouting: ASONs versus static OTNs

IP-based backbone networks are gradually moving to a network model consisting of high-speed routers that are flexibly interconnected by a mesh of light paths set up by an optical transport network that consists of wavelength division multiplexing (WDM) links and optical cross-connects. In such a model, the generalized MPLS protocol suite could provide the IP centric control plane component that will be used to deliver rapid and dynamic circuit provisioning of end-to-end optical light paths between the routers. This is called an automatic switched optical (transport) network (ASON). An ASON enables reconfiguration of the logical IP topology by setting up and tearing down light paths. This allows to up- or downgrade link capacities during a router failure to the capacities needed by the new routing of the affected traffic. Such survivability against (single) IP router failures is cost-effective, as capacity to the IP layer can be provided flexibly when necessary. We present and investigate a logical topology optimization problem that minimizes the total amount or cost of the needed resources (interfaces, wavelengths, WDM line-systems, amplifiers, etc.) in both the IP and the optical layer. A novel optimization aspect in this problem is the possibility, as a result of the ASON, to reuse the physical resources (like interface cards and WDM line-systems) over the different network states (the failure-free and all the router failure scenarios). We devised a simple optimization strategy to investigate the cost of the ASON approach and compare it with other schemes that survive single router failures

A Survey of Network Optimization Techniques for Traffic Engineering

TCP/IP represents the reference standard for the implementation of interoperable communication networks. Nevertheless, the layering principle at the basis of interoperability severely limits the performance of data communication networks, thus requiring proper configuration and management in order to provide effective management of traffic flows. This paper presents a brief survey related to network optimization using Traffic Engineering algorithms, aiming at providing additional insight to the different alternatives available in the scientific literature

Training Curricula for Open Domain Answer Re-Ranking

In precision-oriented tasks like answer ranking, it is more important to rank many relevant answers highly than to retrieve all relevant answers. It follows that a good ranking strategy would be to learn how to identify the easiest correct answers first (i.e., assign a high ranking score to answers that have characteristics that usually indicate relevance, and a low ranking score to those with characteristics that do not), before incorporating more complex logic to handle difficult cases (e.g., semantic matching or reasoning). In this work, we apply this idea to the training of neural answer rankers using curriculum learning. We propose several heuristics to estimate the difficulty of a given training sample. We show that the proposed heuristics can be used to build a training curriculum that down-weights difficult samples early in the training process. As the training process progresses, our approach gradually shifts to weighting all samples equally, regardless of difficulty. We present a comprehensive evaluation of our proposed idea on three answer ranking datasets. Results show that our approach leads to superior performance of two leading neural ranking architectures, namely BERT and ConvKNRM, using both pointwise and pairwise losses. When applied to a BERT-based ranker, our method yields up to a 4% improvement in MRR and a 9% improvement in P@1 (compared to the model trained without a curriculum). This results in models that can achieve comparable performance to more expensive state-of-the-art techniques.Comment: Accepted at SIGIR 2020 (long