Crowdsourcing the mapping problem for design space exploration of custom reconfigurable architecture designs

Authors

  • Anil Kumar Sistla
  • Krunalkumar Patel
  • Gayatri Mehta

DOI:

https://doi.org/10.15346/hc.v2i1.5

Abstract

One of the grand challenges in the design of portable/wearable electronics is to achieve optimal efficiency and flexibility in a tiny low power package. Coarse grained reconfigurable architectures (CGRAs) hold great promise for low power, high performance, and flexible designs for a domain of applications. CGRAs are very promising due to the ability to highly customize such architectures to an application domain. However, greater customization makes the mapping of applications onto these architectures very challenging. Good tools and fast, effective mapping algorithms are needed to support design space exploration for CGRAs. In particular, the mapping problem has been difficult to solve in a satisfying and general way. In this paper, we present an architectural design flow using crowdsourcing to provide mappings of benchmarks onto new architectures. We show that the crowd can provide high quality, reliable mappings, significantly outperforming our custom Simulated Annealing algorithm in almost all cases. We further show that the crowd can provide other types of feedback that are difficult to obtain from an automatic mapping algorithm. Our proof of concept cross-architectural study supports an 8Way or 4Way1Hop architecture as a top choice, concludes that a custom modification that constrains inputs and outputs consumes less energy but requires more area than its less constrained counterpart, and suggests that Stripe architectures are interesting to consider because they perform nearly as well as our mesh variants and may present a more straightforward mapping problem for the crowd or an automatic mapping algorithm.

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Published

2015-08-10

How to Cite

Sistla, A. K., Patel, K., & Mehta, G. (2015). Crowdsourcing the mapping problem for design space exploration of custom reconfigurable architecture designs. Human Computation, 2(1). https://doi.org/10.15346/hc.v2i1.5

Issue

Section

Research