Abstract-Obstacle-avoiding Steiner routing has arisen as a fundamental problem in the physical design of modern VLSI chips. In this paper, we present EBOARST, an efficient four-step algorithm to construct a rectilinear obstacle-avoiding Steiner tree for a given set of pins and a given set of rectilinear obstacles. Our contributions are fourfold. First, we propose a novel algorithm, which generates sparse obstacle-avoiding spanning graphs efficiently. Second, we present a fast algorithm for the minimum terminal spanning tree construction step, which dominates the running time of several existing approaches. Third, we present an edge-based heuristic, which enables us to perform both local and global refinements, leading to Steiner trees with small lengths. Finally, we discuss a refinement technique called segment translation to further enhance the quality of the trees. The time complexity of our algorithm is O(n log n). Experimental results on various benchmarks show that our algorithm achieves 16.56 times speedup on average, while the average length of the resulting obstacle-avoiding rectilinear Steiner trees is only 0.46% larger than the best existing solution.

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With large-scale integration and increasing power densities, thermal management has become an important tool to maintain performance and reliability in modern process technologies. In the core of dynamic thermal management schemes lies accurate reading of on-die temperatures. Therefore, careful planning and embedding of thermal monitoring mechanisms into high-performance systems becomes crucial. In this paper, we propose three techniques to create sensor infrastructures for monitoring the maximum temperature on a multicore system. Initially, we extend a nonuniform sensor placement methodology proposed in the literature to handle chip multiprocessors (CMPs) and show its limitations. We then analyze a grid-based approach where the sensors are placed on a static grid covering each core and show that the sensor readings can differ from the actual maximum core temperature by as much as 12.6 • C when using 16 sensors per core. Also, as large as 10.6% of the thermal emergencies are not captured using the same number of sensors. Based on this observation, we first develop an interpolation scheme, which estimates the maximum core temperature through interpolation of the readings collected at the static grid points. We show that the interpolation scheme improves the measurement accuracy and emergency coverage compared to grid-based placement when using the same number of sensors. Second, we present a dynamic scheme where only a subset of the sensor readings is collected to predict the maximum temperature of each core. Our results indicate that, we can reduce the number of active sensors by as much as 50%, while maintaining similar measurement accuracy and emergency coverage compared to the case where the entire sensor set on the grid is sampled at all times.

Obstacle-avoiding Steiner tree construction is a fundamental problem in VLSI physical design. In this paper, we provide a new approach for rectilinear Steiner tree construction in the presence of obstacles. We propose a novel algorithm, which generates sparse obstacle-avoiding spanning graphs efficiently. We design a fast algorithm for the minimum terminal spanning tree construction, which is the bottleneck step of several existing approaches in terms of running time. We adopt an edge-based heuristic, which enables us to perform both local and global refinement, leading to Steiner trees with small lengths. The time complexity of our algorithm is O(nlogn). Hence, our technique is the most efficient one to the best of our knowledge. Experimental results on various benchmarks show that our algorithm achieves 25.8 times speedup on average, while the average length of the resulting obstacle-avoiding rectilinear Steiner trees is only 1.58% larger than the best existing solution.

Most of the FPGA's area and delay are due to routing. Considering routability at earlier steps of the CAD flow would both yield better quality and faster design process. In this paper, we discuss the metrics that affect routability in packing logic into clusters. We are presenting a routability-driven clustering method for cluster-based FPGAs. Our method packs LUTs into logic clusters while incorporating routability metrics into a cost function. Based on our routability model, the routability in timing-driven packing algorithm is analyzed. We integrate our routability model into a timing-driven packing algorithm. Our method yields up to 50% improvement in terms of the minimum number of routing tracks compared to VPack (16.5% on average). The average routing area improvement is 27% over VPack and 12% over t-VPack.

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The performance of the main memory is an important factor on overall system performance.

In order to maintain performance per Watt in microprocessors, there is a shift towards the chip level multiprocessing paradigm. Microprocessor manufacturers are experimenting with tens of cores, forecasting the arrival of hundreds of cores per single processor die in the near future. With such large-scale integration and increasing power densities, thermal management continues to be a significant design effort to maintain performance and reliability in modern process technologies. In this paper, we present two mechanisms to perform frequency scaling as part of Dynamic Frequency and Voltage Scaling (DVFS) to assist Dynamic Thermal Management (DTM). Our frequency selection algorithms incorporate the physical interaction of the cores on a large-scale system onto the emergency intervention mechanisms for temperature reduction of the hotspot, while aiming to minimize the performance impact of frequency scaling on the core that is in thermal emergency. Our results show that our algorithm consistently succeeds in maximizing the operating frequency of the most critical core while successfully relieving the thermal emergency of the core. A comparison of our two alternative techniques reveals that our physical aware criticality-based algorithm results in 11.7% faster clock frequencies compared to our aggressive scaling algorithm. We also show that our technique is extremely fast and is suited for real time thermal management