Max-PIM: Fast and Efficient Max/Min Searching in DRAM

Recently, in-DRAM computing is becoming one promising technique to address the notorious 'memory-wall' issue for big data processing. In this work, for the first time, we propose a novel 'Min/Max-in-memory' algorithm based on iterative XNOR bit-wise comparison, which supports par...

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Published in:2021 58th ACM/IEEE Design Automation Conference (DAC) pp. 211 - 216
Main Authors: Zhang, Fan, Angizi, Shaahin, Fan, Deliang
Format: Conference Proceeding
Language:English
Published: IEEE 05.12.2021
Subjects:
Big Data
Design automation
Graphics processing units
IMC
In-DRAM Computing
Iterative algorithms
Memory management
Min/Max
Parallel processing
PIM
Random access memory
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Abstract Recently, in-DRAM computing is becoming one promising technique to address the notorious 'memory-wall' issue for big data processing. In this work, for the first time, we propose a novel 'Min/Max-in-memory' algorithm based on iterative XNOR bit-wise comparison, which supports parallel inmemory searching for minimum and maximum of bulk data stored in DRAM as unsigned & signed integers, fixed-point and floating numbers. We then develop a new processing-in-DRAM architecture, called Max-PIM, that supports complete bit-wise Boolean logic and beyond. Differentiating from prior works, Max-PIM is optimized with one-cycle fast XNOR logicin-DRAM operation and in-memory data transpose, which are heavily used and keys to accelerate the proposed Min/Max-in-memory algorithm efficiently. Extensive experiments of utilizing Max-PIM in big data sorting and graph processing applications show that it could speed up ~ 50X and ~ 1000X than GPU and CPU, while only consuming 10% and 1% energy, respectively. Moreover, comparing with recent representative In-DRAM computing platforms, i.e., Ambit [1], DRISA [2], our design could speed up ~ 3X - 10X.
AbstractList Recently, in-DRAM computing is becoming one promising technique to address the notorious 'memory-wall' issue for big data processing. In this work, for the first time, we propose a novel 'Min/Max-in-memory' algorithm based on iterative XNOR bit-wise comparison, which supports parallel inmemory searching for minimum and maximum of bulk data stored in DRAM as unsigned & signed integers, fixed-point and floating numbers. We then develop a new processing-in-DRAM architecture, called Max-PIM, that supports complete bit-wise Boolean logic and beyond. Differentiating from prior works, Max-PIM is optimized with one-cycle fast XNOR logicin-DRAM operation and in-memory data transpose, which are heavily used and keys to accelerate the proposed Min/Max-in-memory algorithm efficiently. Extensive experiments of utilizing Max-PIM in big data sorting and graph processing applications show that it could speed up ~ 50X and ~ 1000X than GPU and CPU, while only consuming 10% and 1% energy, respectively. Moreover, comparing with recent representative In-DRAM computing platforms, i.e., Ambit [1], DRISA [2], our design could speed up ~ 3X - 10X.
Author Angizi, Shaahin
Zhang, Fan
Fan, Deliang
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  givenname: Deliang
  surname: Fan
  fullname: Fan, Deliang
  email: dfan@asu.edu
  organization: Arizona State University,School of Electrical, Computer and Energy Engineering,Tempe,USA
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Snippet Recently, in-DRAM computing is becoming one promising technique to address the notorious 'memory-wall' issue for big data processing. In this work, for the...
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StartPage 211
SubjectTerms Big Data
Design automation
Graphics processing units
IMC
In-DRAM Computing
Iterative algorithms
Memory management
Min/Max
Parallel processing
PIM
Random access memory
Title Max-PIM: Fast and Efficient Max/Min Searching in DRAM
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