Reliability and Energy-efficiency optimizations using Significance-Based Computing
Manufacturing process variability at low geometries and energy dissipation are the most challenging problems in the design of future
computing systems. Currently, manufacturers go to great lengths to guarantee fault-free operation of their products by introducing
redundancy in voltage margins, conservative layout rules, and extra protection circuitry.
However, such design redundancy leading to significant energy overheads may not be really required, given that many modern workloads,
such as multimedia, machine learning, visualization, etc. can tolerate a degree of imprecision in computations and data.
In this talk, I will introduce SCoRPiO, a toolflow which seeks to exploit this observation and to relax reliability requirements
for the hardware layer by allowing a controlled degree of imprecision to be introduced to computations. We show that one can gracefully trade-off
output quality for energy savings, via the degree of unreliable task execution.