Machine learning has enabled significant benefits in diverse fields, but, with a few
exceptions, has had limited impact on computer architecture. Recent work, however, has …

Machine learning (ML) has become a prevalent approach to tame the complexity of design
space exploration for domain-specific architectures. While appealing, using ML for design …

Architects use cycle-by-cycle simulation to evaluate design choices and understand
tradeoffs and interactions among design parameters. Efficiently exploring exponential-size …

The microarchitecture design of a processor has been increasingly difficult due to the large
design space and time-consuming verification flow. Previously, researchers rely on prior …

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 …

Efficiently exploring exponential-size architectural design spaces with many interacting
parameters remains an open problem: the sheer number of experiments required renders …

While cycle-accurate simulators are essential tools for architecture research, design, and
development, their practicality is limited by an extremely long time-to-solution for realistic …

Deploying machine learning models to production is challenging, partially due to the
misalignment between software engineering and machine learning disciplines but also due …

There has been a lot of recent interest in applying machine learning (ML) to the design of
systems, which purports to aid human experts in extracting new insights leading to better …

In engineered systems, the architecture of a system describes how the components of a
system work together to fulfill the system functions and meet stakeholder expectations …