For field-programmable gate arrays (FPGAs), fine-grained pre-computed alternative configurations, combined with simple test-based selection, produce limited per-chip specialization to counter yield loss, increased delay, and increased energy costs that come from fabrication defects and variation. This lightweight approach achieves much of the benefit of knowledge-based full specialization while reducing to practical, palatable levels the computational, testing, and load-time costs that obstruct the application of the knowledge-based approach. In practice this may more than double the power-limited computational capabilities of dies fabricated with 22nm technologies.
Contributions of this work:
• Choose-Your-own-Adventure (CYA), a novel, lightweight, scalable methodology to achieve defect and variation mitigation
• Implementation of CYA, including preparatory components (generation of diverse alternative paths) and FPGA load-time components
• Detailed performance characterization of CYA
– Comparison to conventional loading and dynamic frequency and voltage scaling (DFVS)
– Limit studies to characterize the quality of the CYA implementation and identify potential areas for further optimizatio
