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 …

Cloud applications are increasingly shifting from large monolithic services, to large numbers
of loosely-coupled, specialized microservices. Despite their advantages in terms of …

Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …

Despite its success in many areas, deep learning is a poor fit for use in hardware predictors
because these models are impractically large and slow, but this paper shows how we can …

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

Predicting the number of clock cycles a processor takes to execute a block of assembly
instructions in steady state (the throughput) is important for both compiler designers and …

In this paper, we present DEEPCACHE a novel Framework for content caching, which can
significantly boost cache performance. Our Framework is based on powerful deep recurrent …

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 …

Machine learning (ML) has become the de-facto approach for various scientific domains
such as computer vision and natural language processing. Despite recent breakthroughs …

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 …