  Real-world applications are now processing big-data sets, often bottlenecked
by the data movement between the compute units and the main memory. Near-memory
computing (NMC), a modern data-centric computational paradigm, can alleviate
these bottlenecks, thereby improving the performance of applications. The lack
of NMC system availability makes simulators the primary evaluation tool for
performance estimation. However, simulators are usually time-consuming, and
methods that can reduce this overhead would accelerate the early-stage design
process of NMC systems. This work proposes Near-Memory computing Profiling and
Offloading (NMPO), a high-level framework capable of predicting NMC offloading
suitability employing an ensemble machine learning model. NMPO predicts NMC
suitability with an accuracy of 85.6% and, compared to prior works, can reduce
the prediction time by using hardware-dependent applications features by up to
3 order of magnitude.
