3-D integration technology is one of the leading options to advance Moore’s Law beyond conventional scaling. One of the 3-D integration choice is the heterogeneous integration with the benefits of power saving over the homogeneous integration. With the lack of commercial 3-D tools, existing 3-D physical design flows utilize 2-D commercial tools to perform 3-D integrated circuit (3-D IC) physical synthesis. Specifically, these flows build 2-D designs first and then convert them into 3-D designs. However, several works demonstrate that design qualities degrade during this 2-D–3-D transformation and some of the flows do not support heterogeneous integration. In this article, we propose Snap-3D, a constraint-driven placement approach to build commercial-quality 3-D ICs, which supports both homogeneous and heterogeneous 3-D ICs. Our key idea is based on the observation that if the standard cell height is contracted and partitioned into multiple tiers, any commercial 2-D placer can place them onto the row structure and naturally achieve high-quality 3-D placement. This methodology is shown to optimize power, performance, and area (PPA) metrics across different tiers simultaneously and minimize the aforementioned design quality loss. Experimental results on seven industrial designs demonstrate that Snap-3D achieves up to 10.9% wirelength, 9% power, and 25% performance improvements compared with state-of-the-art 3-D design flows.

This paper describes preliminary results of automatic recognition of Korean broadcast-news speech. We have been working on flexible vocabulary isolated-word speech recognition, and the same HMM models are used for broadcast-news continuous speech recognition. The recognizer is trained by using phonetically balanced isolated words speech, rather than the broadcast news speech itself. In this research, we use several different lexica to investigate the recognition performance according to the length of the words. We also propose a long-distance bigram language model, which can be used at the first stage of the search, so that it can reduce the recognition errors caused by earlier pruning of correct hypothesis. 1.