  Deep neural networks (DNNs) have proven to be effective models for accurate
Memory Access Prediction (MAP), a critical task in mitigating memory latency
through data prefetching. However, existing DNN-based MAP models suffer from
the challenges such as significant physical storage space and poor inference
latency, primarily due to their large number of parameters. These limitations
render them impractical for deployment in real-world scenarios. In this paper,
we propose PaCKD, a Pattern-Clustered Knowledge Distillation approach to
compress MAP models while maintaining the prediction performance. The PaCKD
approach encompasses three steps: clustering memory access sequences into
distinct partitions involving similar patterns, training large pattern-specific
teacher models for memory access prediction for each partition, and training a
single lightweight student model by distilling the knowledge from the trained
pattern-specific teachers. We evaluate our approach on LSTM, MLP-Mixer, and
ResNet models, as they exhibit diverse structures and are widely used for image
classification tasks in order to test their effectiveness in four widely used
graph applications. Compared to the teacher models with 5.406M parameters and
an F1-score of 0.4626, our student models achieve a 552$\times$ model size
compression while maintaining an F1-score of 0.4538 (with a 1.92% performance
drop). Our approach yields an 8.70% higher result compared to student models
trained with standard knowledge distillation and an 8.88% higher result
compared to student models trained without any form of knowledge distillation.
