large language model (LLM)

  • No Comments

SMoLPU: An Energy-Efficient Sparse Mixture-of-Experts-Based Speculative Decoding Language Processing Unit

SMoLPU: An Energy-Efficient Sparse Mixture-of-Experts-Based Speculative Decoding Language Processing Unit 150 150
Author(s): Sangwoo Ha, Jingu Lee, Youngjin Moon, Sunjoo Whang, Wooyoung Jo, Gwangtae Park, Sangjin Kim, Soyeon Um, Junha Ryu, Yurim Jo, Hoi-Jun Yoo

Abstract:

Reasoning strategies such as chain-of-thought improve the accuracy of large language models (LLMs) but lengthen decoding sequences, inflating external memory access (EMA) on mobile devices. Numerous optimizations have been explored to reduce EMA, from conventional techniques such as group quantization and channel-wise pruning to sparse mixture-of-experts (MoE) and speculative decoding (…

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3726852
Publisher: IEEE

View on IEEE Xplore

  • No Comments

High-Precision Close-to-Analog Programming of PCM Cells as Devices for AiMC Edge-AI

High-Precision Close-to-Analog Programming of PCM Cells as Devices for AiMC Edge-AI 150 150
Author(s): Alessio Antolini, Andrea Lico, Francesco Zavalloni, Lorenzo Greco, Riccardo Zurla, Jacopo Bertolini, Riccardo Vignali, Luca Iannelli, Emanuela Calvetti, Marco Pasotti, Alessandro Cabrini, Eleonora Franchi Scarselli

Abstract:

This article presents a high-precision close-to-analog programming methodology for phase-change memory (PCM) cells, targeting analog in-memory computing (AiMC) architectures for edge-artificial intelligence (AI) applications. By leveraging an iterative accumulation approach during the verify phase, the proposed technique enhances the effective number of bits (ENOBs) achievable in PCM cells. Experimental validation …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3680266
Publisher: IEEE

View on IEEE Xplore

  • No Comments

Adelia: A 4-nm LLM Processing Unit With Streamlined Dataflow and Dual-Mode Parallelism for Maximizing Hardware Efficiency

Adelia: A 4-nm LLM Processing Unit With Streamlined Dataflow and Dual-Mode Parallelism for Maximizing Hardware Efficiency 150 150
Author(s): Sukbin Lim, Jung-Hoon Kim, Seungjae Moon, Junseo Cha, Dongjin Seo, Jongho Kim, Hunjong Lee, Jinwon Lee, Joo-Young Kim

Abstract:

The proliferation of large language models (LLMs) as cross-domain foundation models is fueled by aggressive scaling in both parameter counts and inference-time computation. The emergence of sophisticated reasoning models further accelerates this trend, demanding longer context windows and escalating the computational and memory burdens of inference. A fundamental challenge arises …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1513 – 1525
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3663603
Publisher: IEEE

View on IEEE Xplore

Search this website