machine learning (ML)

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Integrating Atomistic Insights With Circuit Simulations via Transformer-Driven Symbolic Regression

Integrating Atomistic Insights With Circuit Simulations via Transformer-Driven Symbolic Regression 150 150
Author(s): Md Rahatul Islam Udoy, Jack Hutchins, Shamiul Alam, Catherine Schuman, Ahmedullah Aziz

Abstract:

This article introduces a framework that establishes a cohesive link between the first principles-based simulations and circuit-level analyses using a machine learning-based compact modeling platform. Starting with atomistic simulations, the framework examines the microscopic details of material behavior, forming the foundation for later stages. The generated datasets, with molecular insights, …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 179 – 187
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3624662
Publisher: IEEE

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Opal: A 16-nm Coarse-Grained Reconfigurable Array SoC for Full Sparse Machine Learning Applications

Opal: A 16-nm Coarse-Grained Reconfigurable Array SoC for Full Sparse Machine Learning Applications 150 150
Author(s): Po-Han Chen, Bo Wun Cheng, Michael Oduoza, Zhouhua Xie, Rupert Lu, Sai Gautham Ravipati, Kalhan Koul, Alex Carsello, Yuchen Mei, Mark Horowitz, Priyanka Raina

Abstract:

Sparsity has recently attracted increased attention in the machine learning (ML) community due to its potential to improve performance and energy efficiency by eliminating ineffectual computations. As ML models evolve rapidly, reconfigurable architectures, such as coarse-grained reconfigurable arrays (CGRAs), are being explored to adapt to and accelerate emerging models. Previous …

Published in: IEEE Solid-State Circuits Letters
Page(s): 293 – 296
Year of Publication: 2025
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2025.3613245
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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