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Physical-synthesis tools are responsible for achieving timing closure. Starting with 130-nm designs, multiple cycles are required to cross the chip, making latch placement critical to success. We present a new physical-synthesis optimization for latch placement called Rip Up and Move Boxes with Linear Evaluation (RUMBLE) that uses a linear timing model to optimize timing by simultaneously replacing multiple gates. RUMBLE runs incrementally and in conjunction with static timing analysis to improve the timing for critical paths that have already been optimized by placement, gate sizing, and buffering. Experimental results validate the effectiveness of the approach: Our techniques improve slack by 41.3% of cycle time on average for a large commercial ASIC design. [ABSTRACT FROM AUTHOR]

The traditional purpose of physical synthesis is to perform timing closure, i.e., to create a placed design that meets its timing specifications while also satisfying electrical, routability, and signal integrity constraints. In modern design flows, physical synthesis tools hardly ever achieve this goal in their first iteration. The design team must iterate by studying the output of the physical synthesis run, then potentially massage the input, e.g., by changing the floorplan, timing assertions, pin locations, logic structures, etc., in order to hopefully achieve a better solution for the next iteration. The complexity of physical synthesis means that systems can take days to run on designs with multimillions of placeable objects, which severely hurts design productivity. This paper discusses some newer techniques that have been deployed within IBM's physical synthesis tool called PDS [1] that significantly improves throughput. In particular, we focus on some of the biggest contributors to runtime, placement, legalization, buffering, and electric correction, and present techniques that generate significant turnaround time improvements. [ABSTRACT FROM AUTHOR]

Placement is a critical component of today's physical-synthesis flow with tremendous impact on the final performance of very large scale integration (VLSI) designs. Unfortunately, it accounts for a significant portion of the overall physical-synthesis runtime. With the complexity and the netlist size of today's VLSI design growing rapidly, clustering for placement can provide an attractive solution to manage affordable placement runtimes. However, such clustering has to be carefully devised to avoid any adverse impact on the final placement solution quality. This paper presents how to apply clustering and unclustering strategies to an analytic top-down placer to achieve large speedups without sacrificing (and sometimes even enhancing) the solution quality. The authors' new bottom-up clustering technique, called the best choice (BC), operates directly on a circuit hypergraph and repeatedly clusters the globally best pair of objects. Clustering score manipulation using a priority-queue (PQ) data structure enables identification of the best pair of objects whenever clustering is performed. To improve the runtime of PQ-based BC clustering, the authors proposed a lazy-update technique for faster updates of the clustering score with almost no loss of the solution quality. A number of effective methods for clustering score calculation, balancing cluster sizes, handling of fixed blocks, and area-based unclustering strategy are discussed. The effectiveness of the resulting hierarchical analytic placement algorithm is tested on several large-scale industrial benchmarks with mixed-size fixed blocks. Experimental results are promising. Compared to the flat analytic placement runs, the hierarchical mode is 2.1 times faster, on the average, with a 1.4% wire-length improvement. [ABSTRACT FROM AUTHOR]

In order to achieve timing closure on increasingly complex IC designs, buffer insertion needs to be performed on thousands of nets within an integrated physical synthesis system. Modern designs may contain large blocks which severely constrain the buffer locations. Even when there may appear to be space for buffers in the alleys between large blocks, these regions are often densely packed or may be needed later to fix critical paths. Therefore, within physical synthesis, a buffer insertion scheme needs to be aware of the porosity of the existing layout to be able to decide when to insert buffers in dense regions to achieve critical performance improvement and when to utilize the sparser regions of the chip. This work addresses the problem of finding porosity-aware buffering solutions by constructing a "smart Steiner tree" to pass to van Ginneken's topology-based algorithm. This flow allows one to fully integrate the algorithm into a physical synthesis system without paying an exorbitant runtime penalty. We show that significant improvements on timing closure are obtained when this approach is integrated into a physical synthesis system. [ABSTRACT FROM AUTHOR]

Recent years have seen significant research in finding closed form expressions for the delay of an RC circuit that improves upon the Elmore delay model. However, several of these formulae assume a step excitation, leaving it to the reader to find a suitable extension to ramp--we always refer to saturated ramps in this paper--inputs. The few works that do consider ramp inputs do not present a closed-form formula that works for a wide range of possible input slews. We propose the PERI (probability distribution function extension for ramp inputs) technique, that extends delay metrics for step inputs to the more general and realistic nonstep (such as a ramp) inputs. Although there has been little work done in finding good slew (which is also referred as signal transition time) metrics, we also show how one can extend a slew metric for step inputs to the nonstep case. We validate the efficacy of our approach through experimental results from several hundred RC dominated nets extracted from an industry application specific integrated circuit design. [ABSTRACT FROM AUTHOR]

IP blocks and large macro cells are becoming more prevalent in the physical layout of a design, actually causing an increase in the available free space. We observe that top-down placement based on recursive bisection with multilevel partitioning performs poorly on these porous designs since it lacks a global view of the ideal placement. However, the strength of analytic placement methods lies in their ability to ascertain this global view. Consequently, we propose an enhancement to cut-based placement called analytic constraint generation (ACG). ACG utilizes an analytic engine to distribute available free space appropriately by determining balance constraints for each partitioning step. For one-dimensional placements, our experiments illustrate the large gap between analytic engines, traditional cut-based placement, and ACG as a design becomes increasingly sparse. We also show that for real industry designs, ACG significantly improves the performance of cut-based placement, particularly timing perspective, as implemented within a state-of-the-art industrial placer. [ABSTRACT FROM AUTHOR]

As technology scales, interconnect-centric design flows become imperative for achieving timing closure. Preplanning buffers and wires in the layout is critical for such flows. Both buffers and wires must be considered simultaneously, since wire routes determine buffer requirements and buffer locations constrain the wire routes. In contrast to recently proposed buffer-block planning approaches, our novel design methodology distributes a set of buffer sites throughout the design. This allows one to use a tile graph to abstract the buffer planning problem and simultaneously address wire planning. We present a four-stage heuristic called resource allocation for buffer and interconnect distribution for resource allocation that includes a new, efficient technique for buffer insertion using a length-based constraint. Extensive experiments validate the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

Physical design is one of the key areas in modern integrated circuit design, and computer-aided design research in this area has shown a new resurgence after the advent of deep submicrometer effects and the accompanying stringent performance constraints. Effects related to increased interconnect congestion effects, reduced noise margins, and growing on-chip inductance have resulted in technically challenging problems in areas such as floorplanning, placement, routing, and performance prediction. In addition, the ability to pack ever more transistors into less silicon challenges the scalability of existing algorithms. In addition to that, the article presents various other related information.