A quantitative study and estimation models for extensible instructions in embedded processors

Designing extensible instructions is a computationally complex task, due to the large design space each instruction is exposed to. One method of speeding up the design cycle is to characterize instructions and estimate their peculiarities during a design exploration. In this paper, we study and derive three estimation models for extensible instructions: area overhead, latency, and power consumption under a wide range of customization parameters. System decomposition and regression analysis are used as the underlying methods to characterize and analyze extensible instructions. We verify our estimation models using automatically and manually generated extensible instructions, plus extensible instructions used in large real-world applications. The mean absolute error of our estimation models arc as small as: 3.4% (6.7% max.) for area overhead, 5.9% (9.4% max.) for latency, and 4.2% (7.2% max.) for power consumption, compared to estimation through the time consuming synthesis and simulation steps using commercial tools. Our estimation models achieve an average speedup of three orders of magnitude over the commercial tools and thus enable us to conduct a fast and extensive design space exploration that would otherwise not be possible. The estimation models are integrated into our extensible processor tool suite.