Digital microfluidic biochips (DMFBs) are gaining increasing attention with promising applications for automating and miniaturizing laboratory procedures in biochemistry. In DMFBs, cross-contamination of droplets with different biomolecules is a major issue, which causes significant errors in bioassays. Washing operations are introduced to clean the cross-contamination spots. However, existing works have oversimplified assumptions on the washing behavior, which either assume infinite washing capacity, or ignore the routing conflicts between functional and washing droplets. This paper proposes the first integrated functional and washing droplet routing flow, which considers practical issues including the finite washing capacity constraint, and the routing conflicts between functional and washing droplets. Washing droplets of different sizes are also proposed to wash the congested cross-contamination spots. Effectiveness of the proposed method is validated by real-life biochemical applications.

In this paper, we consider a typical microgrid scenario that consists of centralized power grid, renewable energy generation, and combined heat and power (CHP) local (co-)generation, as well as power and heat energy storage devices. We aim to minimize the microgrid's operating cost by formulating it as a stochastic non-convex optimization programming, which is challenging to solve optimally. We design an online algorithm by developing a modified Lyapunov optimization approach based on the random system inputs (e.g., the acquired electricity from power grid, the charging/discharging of the energy storage devices, obtained power from the local generator, and the renewable energy generation etc.), which does not require any statistic information of the system. Considering that the nonconvexity of the problem is caused by the dependence of power in battery pack and heat energy in thermal tank, we further explore the relation between them and convert the problem into a convex stochastic optimization programming. We show that the proposed algorithm is efficient with very low computational complexity and is proved to achieve near optimal performance. Moreover, extensive empirical evaluations using real-world traces are provided to study the effectiveness of the proposed algorithm.

Timing analysis is one of the most widely used techniques in digital IC designs. It is increasingly over-loaded by growing circuit sizes and complexities arising from nanometer VLSI technologies. One well-known challenge is process variations, which need to be addressed in timing analysis at least by considering different process corners. Adaptive circuit design, a promising technique for handling variations, further needs statistical static timing analysis (SSTA), which is much more time consuming than variation-oblivious timing analysis. This work explores GPU (Graphic Processing Unit) based parallel computing techniques for accelerating SSTA. Previous works on GPU acceleration are mostly for Monte Carlo based SSTA. By contrast, we focus on SSTA using PCA (Principal Component Analysis), which is intrinsically more efficient. We develop a new batch-based task scheduling algorithm and investigate other speedup techniques such as latency hiding. Experiments are performed on ISPD'13 benchmark suite, which has circuits of over 100K gates. Compared to sequential SSTA, our approach can achieve the identical timing results with average speedup of 22X and 134X on conventional circuits and adaptive circuits, respectively. Our approach is also compared with GPU-based Monte Carlo SSTA and obtains 39X speedup with 0.77% error on timing yield estimation.

In digital microfluidic biochips, cross-contamination of different biomolecule droplets is a major issue. Washing operations are introduced to clean the cross-contamination sites. Existing works have oversimplified assumptions on the washing behavior, which either assume unrealistic infinite washing capacity, or ignore the execution time constraint and/or the routing conflicts between functional and washing droplets. This paper presents the first practical droplet routing flow, which considers realistic issues including the finite washing capacity constraint, and the routing conflicts between washing and functional droplets. Effectiveness of the presented method are validated by real-life biochemical applications.