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Open-source EDA shows promising potential in unleashing EDA innovation and lowering the cost of chip design. This paper presents an open-source EDA project, iEDA, aiming for building a basic infrastructure for EDA technology evolution and closing the industrial-academic gap in the EDA area. iEDA now covers the whole flow of physical design (including Floorplan, Placement, CTS, Routing, Timing Optimization etc.), and part of the analysis tools (Static Timing Analysis and Power Analysis). To demonstrate the effectiveness of iEDA, we implement and tape out three chips of different scales (from 700k to 1.5M gates) on different process nodes (110nm and 28nm) with iEDA. iEDA is publicly available from the project home page http://ieda.oscc.cc.

Poisson's equation has been used in VLSI global placement for describing the potential field caused by a given charge density distribution. Unlike previous global placement methods that solve Poisson's equation numerically, in this paper, we provide an analytical solution of the equation to calculate the potential energy of an electrostatic system. The analytical solution is derived based on the separation of variables method and an exact density function to model the block distribution in the placement region, which is an infinite series and converges absolutely. Using the analytical solution, we give a fast computation scheme of Poisson's equation and develop an effective and efficient global placement algorithm called Pplace. Experimental results show that our Pplace achieves smaller placement wirelength than ePlace and NTUplace3. With the pervasive applications of Poisson's equation in scientific fields, in particular, our effective, efficient, and robust computation scheme for its analytical solution can provide substantial impacts on these fields.

The 0-1 linear programming problem with nonnegative constraint matrix and objective vector e origins from many NP-hard combinatorial optimization problems. In this paper, we consider recovering an optimal solution to the problem from a weighted linear programming.We first formulate the problem equivalently as a sparse optimization problem. Next, we consider the consistency of the optimal solution of the sparse optimization problem and the weighted linear programming problem. In order to achieve this, we establish nonnegative partial s-goodness of the constraint matrix and the weighted vector. Further, we use two quantities to characterize a sufficient condition and necessary condition for the nonnegative partial s-goodness. However, the two quantities are difficult to calculate, therefore, we provide a computable upper bound for one of the two quantities to verify the nonnegative partial s-goodness. Finally, we give three examples to illustrate that our theory is effective and verifiable.

Floorplanning is the first stage of VLSI physical design. An effective floorplanning engine definitely has positive impact on chip design speed, quality and performance. In this paper, we present a novel mathematical model to characterize non-overlapping of modules, and propose a flat fixed-outline floorplanning algorithm based on the VLSI global placement approach using Poisson's equation. The algorithm consists of global floorplanning and legalization phases. In global floorplanning, we redefine the potential energy of each module based on the novel mathematical model for characterizing non-overlapping of modules and an analytical solution of Poisson's equation. In this scheme, the widths of soft modules appear as variables in the energy function and can be optimized. Moreover, we design a fast approximate computation scheme for partial derivatives of the potential energy. In legalization, based on the defined horizontal and vertical constraint graphs, we eliminate overlaps between modules remained after global floorplanning, by modifying relative positions of modules. Experiments on the MCNC, GSRC, HB+ and ami49\_x benchmarks show that, our algorithm improves the average wirelength by at least 2\% and 5\% on small and large scale benchmarks with certain whitespace, respectively, compared to state-of-the-art floorplanners.

In a mobile network, wireless data broadcast over $m$ channels (frequencies) is a powerful means for distributed dissemination of data to clients who access the channels through multi-antennae equipped on their mobile devices. The $\delta$-antennae largest weight data retrieval ($\delta$ALWDR) problem is to compute a schedule for downloading a subset of data items that has a maximum total weight using $\delta$ antennae in a given time interval. In this paper, we propose a ratio $1-\frac{1}{e}-\epsilon$ approximation algorithm for the $\delta$-antennae largest weight data retrieval ($\delta$ALWDR) problem that has the same ratio as the known result but a significantly improved time complexity of $O(2^{\frac{1}{\epsilon}}\frac{1}{\epsilon}m^{7}T^{3.5}L)$ from $O(\epsilon^{3.5}m^{\frac{3.5}{\epsilon}}T^{3.5}L)$ when $\delta=1$ \cite{lu2014data}. To our knowledge, our algorithm represents the first ratio $1-\frac{1}{e}-\epsilon$ approximation solution to $\delta$ALWDR for the general case of arbitrary $\delta$. To achieve this, we first give a ratio $1-\frac{1}{e}$ algorithm for the $\gamma$-separated $\delta$ALWDR ($\delta$A$\gamma$LWDR) with runtime $O(m^{7}T^{3.5}L)$, under the assumption that every data item appears at most once in each segment of $\delta$A$\gamma$LWDR, for any input of maximum length $L$ on $m$ channels in $T$ time slots. Then, we show that we can retain the same ratio for $\delta$A$\gamma$LWDR without this assumption at the cost of increased time complexity to $O(2^{\gamma}m^{7}T^{3.5}L)$. This result immediately yields an approximation solution of same ratio and time complexity for $\delta$ALWDR, presenting a significant improvement of the known time complexity of ratio $1-\frac{1}{e}-\epsilon$ approximation to the problem.