Prenaut: Design space exploration for embedded symmetric multiprocessing with various on-chip architectures

- Prenaut is a design space exploration method for finding the best on-chip architectures given processor cores, Level 1, Level 2, and Level 3 caches.
- Current design space exploration methods mostly explore cache, processor configurations with a fixed architecture. Our method explores architectures with fixed cache and processor configurations.
- Prenaut is to build a data oriented design space exploration method that exploits simulation data to its full extent rather than discarding it.
- Prenaut uses the available simulation data and applies machine learning methods for estimating design parameters.

As embedded systems have evolved to appear in many different domains, symmetric multiprocessing (SMP) has been the design choice from low-end to high-end devices. In this paper we present Prenaut, a design space exploration method for finding the best on-chip SMP architectures given processor cores, Level 1, Level 2, and Level 3 caches. Unlike traditional design space exploration tools that are majorly concerned with optimizations in processor, memory and cache structures with a fixed on-chip architecture, Prenaut explores architectures that have not been considered in symmetric multiprocessing domain. These architectures consist of shared instruction caches between cores and heterogeneous cache topologies that feature bypassing a level in the cache hierarchy. The design idea behind Prenaut is to build a data oriented design space exploration method that exploits simulation data to its full extent rather than discarding it. Therefore, Prenaut uses simulation data and applies machine learning methods for estimating design parameters. This provides very rapid estimation of the Pareto set and guides designers through the overall system design process. The design space is pruned by topological clustering of design points which groups similar topologies and new simulation points are selected via an ordered look up table that prevents infeasible random jumps in the design space. For the selected benchmarks, Prenaut can estimate the Pareto set up to 147x faster and the clustering information can reduce the design space up to 82% in comparison with a state-of-the-art evolutionary algorithm.