High performance algorithms for large scale placement problem

Placement is one of the most important problems in electronic design automation (EDA). An inferior placement solution will not only affect the chip’s performance but might also make it nonmanufacturable by producing excessive wirelength, which is beyond available routing resources. Although placement has been extensively investigated for several decades, it is still a very challenging problem mainly due to that design scale has been dramatically increased by order of magnitudes and the increasing trend seems unstoppable. In modern design, chips commonly integrate millions of gates that require over tens of metal routing layers. Besides, new manufacturing techniques bring out new requests leading to that multi-objectives should be optimized simultaneously during placement. Our research provides high performance algorithms for placement problem. We propose (i) a high performance global placement core engine POLAR; (ii) an efficient routability-driven placer POLAR 2.0, which is an extension of POLAR to deal with routing congestion; (iii) an ultrafast global placer POLAR 3.0, which explore parallelism on POLAR and can make full use of multi-core system; (iv) some efficient triple patterning lithography (TPL) aware detailed placement algorithms

On structure and suboptimality in placement

Abstract — Regular structures are present in many types of circuits. If this structure can be identified and utilized, performance can be improved dramatically. In this paper, we present a novel placement approach that successfully identifies regularity, and obtains placements that are superior to other “general purpose” methods. This method has been integrated into our Feng Shui 2.6 bisection-based placement tool. On experiments with the PEKO benchmarks, our results are within 32 % of optimal for both the large and small suites. The largest example, with 2.1 million cells, can be completed in sixteen hours. The majority of our run time is during detail placement– global placement takes under three hours. The success of our method shows that it can find structure, even when the structure was not expected or intended. As part of this work, we have made a number of observations related to the nature of suboptimality in placement. These observations have shown that some neglected research areas have great potential, while problems that receive considerable attention are essentially adequately solved. I