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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.

|October 26, 2024

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Micromachines·2026

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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.

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.

Keywords:

data allocating algorithm emerging non-volatile memory (NVM)heterogeneous architecture low-power design performance optimization process-in-memory (PIM)

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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.