How can we track synchronized behavior in a stream of time-stamped tuples, such as mobile devices installing and uninstalling applications in the lockstep, to boost their ranks in the app store? We model such tuples as entries in a streaming tensor, which augments attribute sizes in its modes over time. Synchronized behavior tends to form dense blocks (i.e.~subtensors) in such a tensor, signaling anomalous behavior, or interesting communities. However, existing dense block detection methods are either based on a static tensor, or lack an efficient algorithm in a streaming setting. Therefore, we propose a fast streaming algorithm, AUGSPLICING, which can detect the top dense blocks by incrementally splicing the previous detection with the incoming ones in new tuples, avoiding re-runs over all the history data at every tracking time step. AUGSPLICING is based on a splicing condition that guides the algorithm (Section 4). Compared to the state-of-the-art methods, our method is (1) effective to detect fraudulent behavior in installing data of real-world apps and find a synchronized group of students with interesting features in campus Wi-Fi data; (2) robust with splicing theory for dense block detection; (3) streaming and faster than the existing streaming algorithm, with closely comparable accuracy.

In this paper, we propose a novel simulation algorithm for large scale structured power grid networks. The new method formulates the traditional linear system as a special two-dimension Poisson equation and solves it using an analytical expressions based on FFT technique. The computation complexity of the new algorithm is O(NlgN), which is much smaller than the traditional solver's complexity O(N1.5) for sparse matrices, such as the SuperLU solver and the PCG solver. Also, due to the special formulation, graphic process unit (GPU) can be explored to further speed up the algorithm. Experimental results show that the new algorithm is stable and can achieve 100X speed up on GPU over the widely used SuperLU solver with very little memory footprint.

We present two techniques, (1) placement-based timing-driven partitioner (PTP) and (2) virtual physical synthesis based budgeter (VSB), that support effective distributed physical synthesis.

This paper proposes a novel hierarchical approach to the efficient analysis of large VLSI power/ground grids. Different from the existing hierarchical approach where sub-circuit equivalent models are sparsified with computation-intensive integer programming and the resulting modeling may lead to larger errors if the top circuit matrix has large condition number, the new approach employs an iterative (relaxation) procedure to explicitly compensate the errors and avoid introducing dense matrix caused by the circuit reduction. We also propose an efficient scheme for partitioning high performance center-bumped P/G grids. Experimental results demonstrate that the new algorithm is more accurate than the existing hierarchical method while delivering more speedup over the flat simulators.

This paper presents a path-based timing-driven quadratic placement algorithm. The delay of the path acts as the timing constraints. In the global optimization step, it tries to satisfy the timing constraints. In the partition step, it tries to decrease the cut number of critical paths. It has some special skills, such as decrease the delay on the longest path, pad assign, to decrease the delay further. Results show this algorithm can make the timing behavior improve more than 20%.

The timing behavior and congestion behavior are two important goals in the performance-driven standard-cell placement. In this paper, we analyze the relationship between the timing and congestion behavior. We bring up a multi-step placement algorithm to reach the two goals. First, the timing-driven placement algorithm is used to find the global optimal solution. In the second step, the algorithm tries to decrease the maximum congestion while not deteriorating the timing behavior. We have implemented our algorithm and tested it with real circuits. The results show that the maximum delay can decrease by 30% in our timing-driven placement and in the second step the maximum congestion will decrease by 10% while the timing behavior is unchanged.

In this paper, we describe a new congestion-driven placement based on cell inflation. In our approach, we have used the method of probability- estimation to evaluate the routing of nets. We also take use of the strategy of cell inflation to eliminate the routing congestion. Further reduction in congestion is obtained by the scheme of cell moving. We have tested our algorithm on a set of sample circuits from American industry and the results obtained have shown great improvement of routability.