  Nowadays, data-intensive applications are gaining popularity and, together
with this trend, processing-in-memory (PIM)-based systems are being given more
attention and have become more relevant. This paper describes an analytical
modeling tool called Bitlet that can be used, in a parameterized fashion, to
estimate the performance and the power/energy of a PIM-based system and thereby
assess the affinity of workloads for PIM as opposed to traditional computing.
The tool uncovers interesting tradeoffs between, mainly, the PIM computation
complexity (cycles required to perform a computation through PIM), the amount
of memory used for PIM, the system memory bandwidth, and the data transfer
size. Despite its simplicity, the model reveals new insights when applied to
real-life examples. The model is demonstrated for several synthetic examples
and then applied to explore the influence of different parameters on two
systems - IMAGING and FloatPIM. Based on the demonstrations, insights about PIM
and its combination with CPU are concluded.
