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Dynamic Performance and Power Optimization with Heterogeneous Processing-in-Memory for AI Applications on Edge

Sangmin Jeon1, Kangju Lee1, Kyeongwon Lee1

  • 1Department of Intelligent Semiconductor Engineering, Chung-Ang University, 84, Heukseok-ro, Dongjak-gu, Seoul 06974, Republic of Korea.

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Summary
This summary is machine-generated.

This study introduces a novel processing-in-memory (PIM) technology with heterogeneous modules to dynamically optimize artificial intelligence (AI) performance and energy efficiency on edge devices, achieving significant energy savings.

Keywords:
data allocating algorithmemerging non-volatile memory (NVM)heterogeneous architecturelow-power designperformance optimizationprocess-in-memory (PIM)

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Area of Science:

  • Computer Engineering
  • Artificial Intelligence
  • Energy Efficiency

Background:

  • Advancements in AI and Internet of Things (IoT) drive AI applications to edge devices.
  • Balancing performance and energy efficiency for dynamic AI workloads on edge devices is challenging.
  • Existing solutions struggle to adapt to real-time computational load variations.

Purpose of the Study:

  • To propose a novel processing-in-memory (PIM) technology for dynamic optimization of AI applications on edge devices.
  • To address the performance-energy efficiency trade-off under varying computational loads.
  • To enhance AI capabilities at the edge through intelligent resource management.

Main Methods:

  • Developed a heterogeneous PIM architecture with high-performance PIM (HP-PIM) and low-power PIM (LP-PIM) modules.
  • Designed a data placement optimization algorithm to predict workload variations and allocate data.
  • Implemented the PIM architecture in an embedded processor and performed FPGA prototyping.

Main Results:

  • The heterogeneous PIM architecture dynamically adjusts data processing based on workload demands.
  • The data placement algorithm effectively allocates data to optimize energy efficiency.
  • Experimental results show up to 29.54% energy savings for AI applications with varying workloads.

Conclusions:

  • The proposed heterogeneous PIM technology effectively optimizes performance and energy consumption for AI on edge devices.
  • Dynamic workload adaptation is crucial for efficient edge AI deployment.
  • This PIM solution offers a promising approach for sustainable edge AI.