  Data Prefetching is a technique that can hide memory latency by fetching data
before it is needed by a program. Prefetching relies on accurate memory access
prediction, to which task machine learning based methods are increasingly
applied. Unlike previous approaches that learn from deltas or offsets and
perform one access prediction, we develop TransforMAP, based on the powerful
Transformer model, that can learn from the whole address space and perform
multiple cache line predictions. We propose to use the binary of memory
addresses as model input, which avoids information loss and saves a token table
in hardware. We design a block index bitmap to collect unordered future page
offsets under the current page address as learning labels. As a result, our
model can learn temporal patterns as well as spatial patterns within a page. In
a practical implementation, this approach has the potential to hide prediction
latency because it prefetches multiple cache lines likely to be used in a long
horizon. We show that our approach achieves 35.67% MPKI improvement and 20.55%
IPC improvement in simulation, higher than state-of-the-art Best-Offset
prefetcher and ISB prefetcher.
