RWRoute: An Open-source Timing-driven Router for Commercial FPGAs

One of the key obstacles to pervasive deployment of FPGA accelerators in data centers is their cumbersome programming model. Open source tooling is suggested as a way to develop alternative EDA tools to remedy this issue. Open source FPGA CAD tools have traditionally targeted academic hypothetical architectures, making them impractical for commercial devices. Recently, there have been efforts to develop open source back-end tools targeting commercial devices. These tools claim to follow an alternate data-driven approach that allows them to be more adaptable to the domain requirements such as faster compile time. In this paper, we present RWRoute, the first open source timing-driven router for UltraScale+ devices. RWRoute is built on the RapidWright framework and includes the essential and pragmatic features found in commercial FPGA routers that are often missing from open source tools. Another valuable contribution of this work is an open-source lightweight timing model with high fidelity timing approximations. By leveraging a combination of architectural knowledge, repeating patterns, and extensive analysis of Vivado timing reports, we obtain a slightly pessimistic, lumped delay model within 2% average accuracy of Vivado for UltraScale+ devices. Compared to Vivado, RWRoute results in a 4.9× compile time improvement at the expense of 10% Quality of Results (QoR) loss for 665 synthetic and six real designs. A main benefit of our router is enabling fast partial routing at the back-end of a domain-specific flow. Our initial results indicate that more than 9× compile time improvement is achievable for partial routing. The results of this paper show how such a router can be beneficial for a low touch flow to reduce dependency on commercial tools.

Handling Iterations in Distributed Dataflow Systems

Over the past decade, distributed dataflow systems (DDS) have become a standard technology. In these systems, users write programs in restricted dataflow programming models, such as MapReduce, which enable them to scale out program execution to a shared-nothing cluster of machines. Yet, there is no established consensus that prescribes how to extend these programming models to support iterative algorithms. In this survey, we review the research literature and identify how DDS handle control flow, such as iteration, from both the programming model and execution level perspectives. This survey will be of interest for both users and designers of DDS.

Repair limit policy of aircraft component based on extended uncertain random renewal reward process

PurposeThe purpose of this paper is to focus on an optimizing maintenance policy with repair limit time for a new type of aircraft component, in which the lifetime is assumed to be an uncertain variable due to no historical operation data, and the repair time is a random variable that can be described by the experimental data.Design/methodology/approachTo describe this repair limit time policy over an infinite time horizon, an extended uncertain random renewal reward theorem is firstly proposed based on chance theory, involves uncertain random interarrival times and stochastic rewards. Accordingly, the uncertain random programming model, which minimized the expected maintenance cost rate, is formulated to find the optimal repair limit time.FindingsA numerical example with sensitivity analysis is provided to illustrate the utility of the proposed policy. It provides a useful reference and guidance for aircraft optimization. For maintainers, it plays an important guiding role in engineering practice.Originality/valueThe proposed uncertain random renewal reward process proved useful for the optimization of maintenance strategy with maintenance limited time for a new type of aircraft components, which provides scientific support for aircraft maintenance decision-making for civil aviation enterprises.

A Novel Mathematical Model for Radio Mean Square Labeling Problem

A radio mean square labeling of a connected graph is motivated by the channel assignment problem for radio transmitters to avoid interference of signals sent by transmitters. It is an injective map h from the set of vertices of the graph G to the set of positive integers N , such that for any two distinct vertices x , y , the inequality d x , y + h x 2 + h y 2 / 2 ≥ dim G + 1 holds. For a particular radio mean square labeling h , the maximum number of h v taken over all vertices of G is called its spam, denoted by rmsn h , and the minimum value of rmsn h taking over all radio mean square labeling h of G is called the radio mean square number of G , denoted by rmsn G . In this study, we investigate the radio mean square numbers rmsn P n and rmsn C n for path and cycle, respectively. Then, we present an approximate algorithm to determine rmsn G for graph G . Finally, a new mathematical model to find the upper bound of rmsn G for graph G is introduced. A comparison between the proposed approximate algorithm and the proposed mathematical model is given. We also show that the computational results and their analysis prove that the proposed approximate algorithm overcomes the integer linear programming model (ILPM) according to the radio mean square number. On the other hand, the proposed ILPM outperforms the proposed approximate algorithm according to the running time.

Optimization of Humanitarian Aid Distribution in Case of an Earthquake and Tsunami in the City of Iquique, Chile

In this paper, we introduce, model, and solve a clustered resource allocation and routing problem for humanitarian aid distribution in the event of an earthquake and subsequent tsunami. First, for the preparedness stage, we build a set of clusters to identify, classify, sort, focus, and prioritize the aid distribution. The clusters are built with k-means method and a modified version of the capacitated p-median model. Each cluster has a set of beneficiaries and candidate delivery aid points. Second, vehicle routes are strategically determined to visit the clusters for the response stage. A mixed integer linear programming model is presented to determine efficient vehicle routes, minimizing the aid distribution times. A vulnerability index is added to our model to prioritize aid distribution. A case study is solved for the city of Iquique, Chile.

Determining the Optimal Density of Phoebe bournei Plantations Based on Dynamic Programming under Close-to-Nature Management Measures

Close-to-nature management (CTNM) is the most promising option for plantation silviculture and has received widespread attention in recent years. Stand density is a key variable in CTNM, as it directly influences growth and yield. Research for the optimal density that maximizes the total harvest has been ongoing. In this paper, a dynamic programming model was applied to the CTNM of Phoebe bournei plantations for the first time to solve the problem of stand density and target tree density control. This paper took Phoebe bournei plantations in Jindong Forest Farm of Hunan Province as the research object. Based on the data of seven consecutive years from 2015 to 2021, Richard’s growth equation was used to fit the height growth equation and basal area growth equation of Phoebe bournei. Stand growth was divided into five development stages according to the forest growth process and characteristics. Stand density and basal area were selected as two-dimensional state variables, and the maximum total harvest in the entire stand growth process was used as the objective function to establish a dynamic programming model. The optimal stand density and target tree density at each growth stage of the stand under three different site conditions were determined. According to the results obtained, the objective forest shape was designed for the stand under three types of site conditions, which can provide a theoretical basis for the CTNM of Phoebe bournei plantations to make the stand achieve the maximum harvest.