Incremental Lagrangian Relaxation Based Discrete Gate Sizing and Threshold Voltage Assignment

Timing closure remains one of the most critical challenges of a physical synthesis flow, especially when the design operates under multiple operating conditions. Even if timing is almost closed at the end of the flow, last-mile placement and routing congestion optimizations may introduce new timing violations. Correcting such violations needs minimally disruptive techniques such as threshold voltage reassignment and gate sizing that affect only marginally the placement and routing of the almost finalized design. To this end, we transform a powerful Lagrangian-relaxation-based optimizer, used for global timing optimization early in the design flow, into a practical incremental timing optimizer that corrects small timing violations with fast runtime and without increasing the area/power of the design. The proposed approach was applied to already optimized designs of the ISPD 2013 benchmarks assuming that they experience new timing violations due to local wire rerouting. Experimental results show that in single corner designs, timing is improved by more than 36% on average, using 45% less runtime. Correspondingly, in a multicorner context, timing is improved by 39% when compared to the fully-fledged version of the timing optimizer.

Congestion Prediction in FPGA Using Regression Based Learning Methods

Design closure in general VLSI physical design flows and FPGA physical design flows is an important and time-consuming problem. Routing itself can consume as much as 70% of the total design time. Accurate congestion estimation during the early stages of the design flow can help alleviate last-minute routing-related surprises. This paper has described a methodology for a post-placement, machine learning-based routing congestion prediction model for FPGAs. Routing congestion is modeled as a regression problem. We have described the methods for generating training data, feature extractions, training, regression models, validation, and deployment approaches. We have tested our prediction model by using ISPD 2016 FPGA benchmarks. Our prediction method reports a very accurate localized congestion value in each channel around a configurable logic block (CLB). The localized congestion is predicted in both vertical and horizontal directions. We demonstrate the effectiveness of our model on completely unseen designs that are not initially part of the training data set. The generated results show significant improvement in terms of accuracy measured as mean absolute error and prediction time when compared against the latest state-of-the-art works.

Delay Tolerant Networks

Opportunistic networks are one of the emerging evolutions of the network system. In opportunistic networks, nodes are able to communicate with each other even if the route between source to destination does not already exist. Opportunistic networks have to be delay tolerant in nature (i.e., able to tolerate larger delays). Delay tolerant network (DTNs) uses the concept of “store-carry-forward” of data packets. DTNs are able to transfer data or establish communication in remote area or crisis environment where there is no network established. DTNs have many applications like to provide low-cost internet provision in remote areas, in vehicular networks, noise monitoring, extreme terrestrial environments, etc. It is therefore very promising to identify aspects for integration and inculcation of opportunistic network methodologies and technologies into delay tolerant networking. In this chapter, the authors emphasize delay tolerant networks by considering its architectural, routing, congestion, and security issues.

An Application of Big Data Analytics in Road Transportation

Data is being captured in all domains of society and one of the important aspects is transportation. Large amounts of data have been collected, which are detailed, fine-grained, and of greater coverage and help us to allow traffic and transportation to be tracked to an extent that was not possible in the past. Existing big data analytics for transportation is already yielding useful applications in the areas of traffic routing, congestion management, and scheduling. This is just the origin of the applications of big data that will ultimately make the transportation network able to be managed properly and in an efficient way. It has been observed that so many individuals are not following the traffic rules properly, especially where there are high populations, so to monitor theses types of traffic violators, this chapter proposes a work that is mainly based on big data analytics. In this chapter, the authors trace the vehicle and the data that has been collected by different devices and analyze it using some of the big data analysis methods.