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 …
Pythia: A customizable hardware prefetching framework using online reinforcement learning
Past research has proposed numerous hardware prefetching techniques, most of which rely
on exploiting one specific type of program context information (eg, program counter …
on exploiting one specific type of program context information (eg, program counter …
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 …
Pixel: Photonic neural network accelerator
Machine learning (ML) architectures such as Deep Neural Networks (DNNs) have achieved
unprecedented accuracy on modern applications such as image classification and speech …
unprecedented accuracy on modern applications such as image classification and speech …
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 …
IntelliNoC: A holistic design framework for energy-efficient and reliable on-chip communication for manycores
As technology scales, Network-on-Chips (NoCs), currently being used for on-chip
communication in manycore architectures, face several problems including high network …
communication in manycore architectures, face several problems including high network …
Experiences with ml-driven design: A noc case study
J Yin, S Sethumurugan, Y Eckert… - … Symposium on High …, 2020 - ieeexplore.ieee.org
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 …
systems, which purports to aid human experts in extracting new insights leading to better …
Cure: A high-performance, low-power, and reliable network-on-chip design using reinforcement learning
K Wang, A Louri - IEEE Transactions on Parallel and …, 2020 - ieeexplore.ieee.org
We propose CURE, a deep reinforcement learning (DRL)-based NoC design framework that
simultaneously reduces network latency, improves energy-efficiency, and tolerates transient …
simultaneously reduces network latency, improves energy-efficiency, and tolerates transient …
Deep reinforcement learning enabled self-configurable networks-on-chip for high-performance and energy-efficient computing systems
MF Reza - IEEE Access, 2022 - ieeexplore.ieee.org
Network-on-Chips (NoC) has been the superior interconnect fabric for multi/many-core on-
chip systems because of its scalability and parallelism. On-chip network resources can be …
chip systems because of its scalability and parallelism. On-chip network resources can be …
ALPHA: A learning-enabled high-performance network-on-chip router design for heterogeneous manycore architectures
Y Li, A Louri - IEEE Transactions on Sustainable Computing, 2020 - ieeexplore.ieee.org
Heterogeneous manycores comprised of CPUs, GPUs and accelerators are putting stringent
demands on network-on-chips (NoCs). The NoCs need to support the combined traffic …
demands on network-on-chips (NoCs). The NoCs need to support the combined traffic …