It has been a long time that computer architecture and systems are optimized for efficient
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …

Public cloud providers seek to meet stringent performance requirements and low hardware
cost. A key driver of performance and cost is main memory. Memory pooling promises to …

Machine-learned predictors, although achieving very good results for inputs resembling
training data, cannot possibly provide perfect predictions in all situations. Still, decision …

Past research has proposed numerous hardware prefetching techniques, most of which rely
on exploiting one specific type of program context information (eg, program counter …

Program execution speed critically depends on increasing cache hits, as cache hits are
orders of magnitude faster than misses. To increase cache hits, we focus on the problem of …

This paper presents Voyager, a novel neural network for data prefetching. Unlike previous
neural models for prefetching, which are limited to learning delta correlations, our model can …

Recent years have seen a dramatic increase in the microarchitectural complexity of
processors. This increase in complexity presents a twofold challenge for the field of …

Recently, the machine learning research trend expands to the system performance
optimization field, where it has still been proposed by researchers based on their intuitions …

Memory disaggregation (MD) allows for scalable and elastic data center design by
separating compute (CPU) from memory. With MD, compute and memory are no longer …

Extensive research has been carried out to improve cache replacement policies, yet
designing an efficient cache replacement policy that incurs low hardware overhead remains …