Multi-Layer In-Memory Processing

In-memory computing provides revolutionary changes to computer architecture by fusing memory and computation, allowing data-intensive computations to reduce data communications. Despite promising results of in-memory computing in each layer of the memory hierarchy, an integrated approach to a system...

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Veröffentlicht in:2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO) S. 920 - 936
Hauptverfasser: Fujiki, Daichi, Khadem, Alireza, Mahlke, Scott, Das, Reetuparna
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.10.2022
Schlagworte:
accelerator
Energy efficiency
GNN
Graph neural networks
Graphics processing units
In-memory computing
Microarchitecture
Performance evaluation
processing in memory
Processor scheduling
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Zusammenfassung:In-memory computing provides revolutionary changes to computer architecture by fusing memory and computation, allowing data-intensive computations to reduce data communications. Despite promising results of in-memory computing in each layer of the memory hierarchy, an integrated approach to a system with multiple computable memories has not been examined. This paper presents a holistic and application-driven approach to building Multi-Layer In-Memory Processing (MLIMP) systems, enabling applications with variable computation demands to reap the benefits of heterogeneous compute resources in an integrated MLIMP system. By introducing concurrent task scheduling to MLIMP, we achieve improved performance and energy efficiency for graph neural networks and multiprogramming of data parallel applications.
DOI:10.1109/MICRO56248.2022.00068