AI system outperforms humans in designing floorplans for microchips
Andrew B. Kahng is in the Department of Computer Science and Engineering, and in the Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, California 92093, USA.
You can also search for this author in PubMed Google Scholar
Success or failure in designing microchips depends heavily on steps known as floorplanning and placement. These steps determine where memory and logic elements are located on a chip. The locations, in turn, strongly affect whether the completed chip design can satisfy operational requirements such as processing speed and power efficiency. So far, the floorplanning task, in particular, has defied all attempts at automation. It is therefore performed iteratively and painstakingly, over weeks or months, by expert human engineers. But in a paper in Nature, researchers from Google (Mirhoseini et al.1) report a machine-learning approach that achieves superior chip floorplanning in hours.
Modern chips are a miracle of technology and economics, with billions of transistors laid out and interconnected on a piece of silicon the size of a fingernail. Each chip can contain tens of millions of logic gates, called standard cells, along with thousands of memory blocks, known as macro blocks, or macros. The cells and macro blocks are interconnected by tens of kilometres of wiring to achieve the designed functionality. Given this staggering complexity, the chip-design process itself is another miracle — in which the efforts of engineers, aided by specialized software tools, keep the complexity in check.
