PATHFINDER: Practical Real-Time Learning for Data Prefetching

Data prefetching is vital in high-performance processors and a large body of research has introduced a number of different approaches for accurate prefetching: stride detection, address correlating prefetchers, delta pattern detection, irregular pattern detection, etc. Most recently, a few works have leveraged advances in machine learning and deep neural networks to design prefetchers. These neural-inspired prefetchers observe data access patterns and develop a trained model that can then make accurate predictions for future accesses. A significant impediment to the success of these prefetchers is their high implementation cost, for both inference and training. These models cannot be trained in real-time, i.e., they have to be trained beforehand with a large benchmark suite. This results in a large model (that increases the overhead for inference), and the model can only successfully predict patterns that are similar to patterns in the training set.

In this work, we explore the potential of using spiking neural networks to learn and predict data access patterns, and specifically address deltas. While prior work has leaned on the recent success of trained artificial neural networks, we hypothesize that spiking neural networks are a better fit for real-time data prefetching. Spiking neural networks rely on the STDP algorithm to learn while performing inference -it is a low-cost and local learning algorithm that can quickly observe and react to the current stream of accesses. It is therefore possible to achieve high accuracy on previously unseen access patterns with a relatively small