  Recent advances in 3D fabrication have allowed handling the memory
bottlenecks for modern data-intensive applications by bringing the computation
closer to the memory, enabling Near Memory Processing (NMP). Memory Centric
Networks (MCN) are advanced memory architectures that use NMP architectures,
where multiple stacks of the 3D memory units are equipped with simple
processing cores, allowing numerous threads to execute concurrently. The
performance of the NMP is crucially dependent upon the efficient task
offloading and task-to-NMP allocation. Our work presents a multi-armed bandit
(MAB) based approach in formulating an efficient resource allocation strategy
for MCN. Most existing literature concentrates only on one application domain
and optimizing only one metric, i.e., either execution time or power. However,
our solution is more generic and can be applied to diverse application domains.
In our approach, we deploy Upper Confidence Bound (UCB) policy to collect
rewards and eventually use it for regret optimization. We study the following
metrics: instructions per cycle, execution times, NMP core cache misses, packet
latencies, and power consumption. Our study covers various applications from
PARSEC and SPLASH2 benchmarks suite. The evaluation shows that the system's
performance improves by ~11% on average and an average reduction in total power
consumption by ~12%.
