1 research outputs found
Generalized Data Placement Strategies for Racetrack Memories
Ultra-dense non-volatile racetrack memories (RTMs) have been investigated at
various levels in the memory hierarchy for improved performance and reduced
energy consumption. However, the innate shift operations in RTMs hinder their
applicability to replace low-latency on-chip memories. Recent research has
demonstrated that intelligent placement of memory objects in RTMs can
significantly reduce the amount of shifts with no hardware overhead, albeit for
specific system setups. However, existing placement strategies may lead to
sub-optimal performance when applied to different architectures. In this paper
we look at generalized data placement mechanisms that improve upon existing
ones by taking into account the underlying memory architecture and the timing
and liveliness information of memory objects. We propose a novel heuristic and
a formulation using genetic algorithms that optimize key performance
parameters. We show that, on average, our generalized approach improves the
number of shifts, performance and energy consumption by 4.3x, 46% and 55%
respectively compared to the state-of-the-art.Comment: 6 pages, 6 figure