Exploring optimal cost-performance designs for Raw microprocessors

The semiconductor industry roadmap projects that advance in VLSI technology will permit more than one billion transistors on a chip by the year 2010. The MIT Raw microprocessor is a proposed architecture that strives to exploit these chip-level resources by implementing thousands of tiles, each comprising a processing element and a small amount of memory, coupled by a static two-dimensional interconnect. A compiler partitions fine-grain instruction-level parallelism across the tiles and statically schedules inter-tile communication over the interconnect. Because Raw microprocessors fully expose their internal hardware structure to the software, they can be viewed as a gigantic FPGA with coarse-grained tiles, in which software orchestrates communication over static interconnections. One open challenge in Raw architectures is to determine their optimal grain size and balance. The grain size is the area of each tile, and the balance is the proportion of area in each tile devoted to memory, processing, communication, and I/O. If the total chip area is fixed, more area devoted to processing will result in a higher processing power per node, but will lead to a fewer number of tiles. This paper presents an analytical framework using which designers can reason about the design space of Raw microprocessors. Based on an architectural model and a VLSI cost analysis, the framework computes the performance of applications, and uses an optimization process to identify designs that will execute these applications most cost-effectively.