  This paper describes an analytical modeling tool called Bitlet that can be
used, in a parameterized fashion, to understand the affinity of workloads to
processing-in-memory (PIM) as opposed to traditional computing. The tool
uncovers interesting trade-offs between operation complexity (cycles required
to perform an operation through PIM) and other key parameters, such as system
memory bandwidth, data transfer size, the extent of data alignment, and
effective memory capacity involved in PIM computations. Despite its simplicity,
the model has already proven useful. In the future, we intend to extend and
refine Bitlet to further increase its utility.
