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 …

In the last decade, machine-learning-based compilation has moved from an obscure
research niche to a mainstream activity. In this paper, we describe the relationship between …

GPUs have become prevalent and more general purpose, but GPU programming remains
challenging and time consuming for the majority of programmers. In addition, it is not always …

A mechanistic model for out-of-order superscalar processors is developed and then applied
to the study of microarchitecture resource scaling. The model divides execution time into …

Asymmetric multi-core architectures have recently emerged as a promising alternative in a
power and thermal constrained environment. They typically integrate cores with different …

Application-specific multiprocessor systems-on-chip (MPSoCs) are usually designed by
using a platform-based approach, where a wide range of customizable parameters can be …

Graphics processing units (GPUs) are of increasing interest because they offer massive
parallelism for high-throughput computing. While GPUs promise high peak performance …

Online Reinforcement Learning (RL) has been adopted as an effective mechanism in
various decision-making problems in microarchitecture. Its high adaptability and the ability to …

Adaptive micro architectures are a promising solution for designing high-performance,
power-efficient microprocessors. They offer the ability to tailor computational resources to …

Fast and accurate performance and power prediction is a key challenge in co-development
of hardware and software. Traditional analytical or simulation-based approaches are often …