inference

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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

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Energy-Efficient Reconfigurable XGBoost Inference Accelerator With Modular Unit Trees via Selective Node Execution and Data Movement

Energy-Efficient Reconfigurable XGBoost Inference Accelerator With Modular Unit Trees via Selective Node Execution and Data Movement 150 150
Author(s): Chang Eun Song, Yidong Li, Amardeep Ramnani, Pulkit Agrawal, Purvi Agrawal, Sung-Joon Jang, Sang-Seol Lee, Tajana Rosing, Mingu Kang

Abstract:

The extreme gradient boosting (XGBoost) has emerged as a powerful AI algorithm, achieving high accuracy and winning multiple Kaggle competitions in various tasks including medical diagnosis, recommendation systems, and autonomous driving. It has great potential for running on edge devices due to its binary tree-based simple computing kernel, offering unique …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3607896
Publisher: IEEE

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MINOTAUR: A Posit-Based 0.42–0.50-TOPS/W Edge Transformer Inference and Training Accelerator

MINOTAUR: A Posit-Based 0.42–0.50-TOPS/W Edge Transformer Inference and Training Accelerator 150 150
Author(s): Kartik Prabhu, Robert M. Radway, Jeffrey Yu, Kai Bartolone, Massimo Giordano, Fabian Peddinghaus, Yonatan Urman, Win-San Khwa, Yu-Der Chih, Meng-Fan Chang, Subhasish Mitra, Priyanka Raina

Abstract:

Transformer models have revolutionized natural language processing (NLP) and enabled many new applications, but are challenging to deploy on resource-constrained edge devices due to their high computation and memory demands. We present MINOTAUR, an edge system-on-chip (SoC) for inference and fine-tuning of Transformer models with all memory on the chip. …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1311 – 1323
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3545731
Publisher: IEEE

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