WaveScalar

Silicon technology continues to provide an exponential increase in the availability of raw transistors. Effectively translating this resource into application performance, however, is an open challenge. Ever increasing wire-delay relative to switching speed and the exponential cost of circuit complexity make simply scaling up existing processor designs futile. In this paper, we present an alternative to superscalar design, WaveScalar. WaveScalar is a dataflow instruction set architecture and execution model designed for scalable, low-complexity/high performance processors. WaveScalar is unique among dataflow architectures in efficiently providing traditional memory semantics. At last, a dataflow machine can run "real-world" programs, written in any language, without sacrificing parallelism. The WaveScalar ISA is designed to run on an intelligent memory system. Each instruction in a WaveScalar binary executes in place in the memory system and explicitly communicates with its dependents in dataflow fashion. WaveScalar architectures cache instructions and the values they operate on in a WaveCache, a simple grid of "alu-in-cache" nodes. By co-locating computation and data in physical space, the WaveCache minimizes long wire, high-latency communication. This paper introduces the WaveScalar instruction set and evaluates a simulated implementation based on current technology. Results for the SPEC and Mediabench applications demonstrate that the WaveCache out-performs an aggressively configured superscalar design by 2-7 times, with ample opportunities for future optimizations.