A survey of machine learning for computer architecture and systems
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 …
execution of machine learning (ML) models. Now, it is time to reconsider the relationship …
A survey on energy management for mobile and IoT devices
Mobile and IoT devices have proliferated our daily lives. However, these miniaturized
computing systems should be highly energy-efficient due to their ultrasmall form factor …
computing systems should be highly energy-efficient due to their ultrasmall form factor …
Load value approximation
Approximate computing explores opportunities that emerge when applications can tolerate
error or inexactness. These applications, which range from multimedia processing to …
error or inexactness. These applications, which range from multimedia processing to …
Adapt-noc: A flexible network-on-chip design for heterogeneous manycore architectures
The increased computational capability in heterogeneous manycore architectures facilitates
the concurrent execution of many applications. This requires, among other things, a flexible …
the concurrent execution of many applications. This requires, among other things, a flexible …
An energy-aware online learning framework for resource management in heterogeneous platforms
Mobile platforms must satisfy the contradictory requirements of fast response time and
minimum energy consumption as a function of dynamically changing applications. To …
minimum energy consumption as a function of dynamically changing applications. To …
A deep reinforcement learning framework for architectural exploration: A routerless NoC case study
Machine learning applied to architecture design presents a promising opportunity with broad
applications. Recent deep reinforcement learning (DRL) techniques, in particular, enable …
applications. Recent deep reinforcement learning (DRL) techniques, in particular, enable …
An energy-efficient network-on-chip design using reinforcement learning
H Zheng, A Louri - Proceedings of the 56th Annual Design Automation …, 2019 - dl.acm.org
The design space for energy-efficient Network-on-Chips (NoCs) has expanded significantly
comprising a number of techniques. The simultaneous application of these techniques to …
comprising a number of techniques. The simultaneous application of these techniques to …
High-performance, energy-efficient, fault-tolerant network-on-chip design using reinforcement learning
Network-on-Chips (NoCs) are becoming the standard communication fabric for multi-core
and system on a chip (SoC) architectures. As technology continues to scale, transistors and …
and system on a chip (SoC) architectures. As technology continues to scale, transistors and …
Uncore power scavenger: A runtime for uncore power conservation on hpc systems
The US Department of Energy (DOE) has set a power target of 20-30MW on the first
exascale machines. To achieve one exaflop under this power constraint, it is necessary to …
exascale machines. To achieve one exaflop under this power constraint, it is necessary to …
A survey of machine learning applied to computer architecture design
DD Penney, L Chen - arxiv preprint arxiv:1909.12373, 2019 - arxiv.org
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 …
exceptions, has had limited impact on computer architecture. Recent work, however, has …