In-memory data parallel processor

Daichi Fujiki, Scott Mahlke, Reetuparna Das

Research output: Chapter in Book/Report/Conference proceedingConference contribution

45 Citations (Scopus)

Abstract

Recent developments in Non-Volatile Memories (NVMs) have opened up a new horizon for in-memory computing. Despite the significant performance gain offered by computational NVMs, previous works have relied on manual mapping of specialized kernels to the memory arrays, making it infeasible to execute more general workloads. We combat this problem by proposing a programmable in-memory processor architecture and data-parallel programming framework. The efficiency of the proposed in-memory processor comes from two sources: massive parallelism and reduction in data movement. A compact instruction set provides generalized computation capabilities for the memory array. The proposed programming framework seeks to leverage the underlying parallelism in the hardware by merging the concepts of data-flow and vector processing. To facilitate in-memory programming, we develop a compilation framework that takes a TensorFlow input and generates code for our inmemory processor. Our results demonstrate 7.5× speedup over a multi-core CPU server for a set of applications from Parsec and 763× speedup over a server-class GPU for a set of Rodinia benchmarks.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2018
PublisherAssociation for Computing Machinery
Pages1-14
Number of pages14
Volume53
Edition2
ISBN (Electronic)9781450349116
DOIs
Publication statusPublished - 2018 Mar 19
Externally publishedYes
Event23rd International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2018 - Williamsburg, United States
Duration: 2018 Mar 242018 Mar 28

Conference

Conference23rd International Conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2018
Country/TerritoryUnited States
CityWilliamsburg
Period18/3/2418/3/28

ASJC Scopus subject areas

  • Computer Science(all)

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