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Mason Hill

dblp:207/1819 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
GPUs and heterogeneous computing · 100%
Artificial intelligence
1 paper
Deep learning architectures and training · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
GPUs and heterogeneous computing
deep learning on GPUs		 0.312017
DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission · MICRO 2017
Machine learning › Deep learning architectures and training › neural network inference
DNN inference		 0.112017
DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission · MICRO 2017

Methods — techniques the papers use, named apart from their topics

synapse vector elimination · 0.6data fission · 0.6
YearPublicationVenuePosition
2017 DeftNN: addressing bottlenecks for DNN execution on GPUs via synapse vector elimination and near-compute data fission
abstract
Deep neural networks (DNNs) are key computational building blocks for emerging classes of web services that interact in real time with users via voice, images and video inputs. Although GPUs have gained popularity as a key accelerator platform for deep learning workloads, the increasing demand for DNN computation leaves a significant gap between the compute capabilities of GPU-enabled datacenters and the compute needed to service demand.
Parker Hill, Animesh Jain, Mason Hill, Babak Zamirai, Chang-Hong Hsu, Michael Laurenzano, Scott A. Mahlke, Lingjia Tang, Jason Mars
 MICRO3