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A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integration

A3D-MoE: Acceleration of Large Language Models with Mixture of Experts via 3D Heterogeneous Integration 150 150
Author(s): Wei-Hsing Huang, Janak Sharda, Cheng-Jhih Shih, Yuyao Kong, Faaiq Waqar, Pin-Jun Chen, Yingyan Lin, Shimeng Yu

Abstract:

Conventional large language models (LLMs) have large parameter sets, making inference costly and energy-intensive; Mixture-of-Experts (MoE) mitigates this by activating fewer weights per token, but fine-grained MoE LLMs still face runtime workload variability, inefficient conventional scheduling, and high HBM loading energy/bandwidth demands. A3D-MoE addresses these with 3D heterogeneous …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 1 – 1
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3713957
Publisher: IEEE

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Leveraging a Passive MRAM Crossbar for Hardware-in-the-Loop and Continual learning

Leveraging a Passive MRAM Crossbar for Hardware-in-the-Loop and Continual learning 150 150
Author(s): M. Drouhin, M. Grelier, M. Deroo, Y. Sassi, E. Monteblanco, N. Davila, G. Mihajlović, R. V. Chopdekar, T. S. Santos, J. A. Katine, D. Ravelosona

Abstract:

Artificial neural networks (ANNs) have enabled major advances in artificial intelligence, yet their growing computational and energy demands challenge conventional von Neumann architectures due to the costly separation of memory and processing. In-memory computing has emerged as a promising solution, particularly through memristive crossbar arrays capable of performing multiply-and-accumulate operations …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 129 – 134
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3702001
Publisher: IEEE

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