Accelerator

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LUT-Based Convolutional Tsetlin Machine Accelerator with Dynamic Clause Scaling for Resources Constrained FPGAs

LUT-Based Convolutional Tsetlin Machine Accelerator with Dynamic Clause Scaling for Resources Constrained FPGAs 150 150
Author(s): Rashed Al Amin, Roman Obermaisser

Abstract:

The rapid growth of machine learning (ML) workloads, particularly in computer vision applications, has significantly increased computational and energy demands in modern electronic systems, motivating the use of hardware accelerators to offload processing from general-purpose processors. Despite advances in computationally efficient ML models, achieving energy-efficient inference on resource-constrained edge devices …

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.2026.3676833
Publisher: IEEE

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Birch: A Real-Time Multi-Domain Multi-Task Extended Reality Perception Accelerator

Birch: A Real-Time Multi-Domain Multi-Task Extended Reality Perception Accelerator 150 150
Author(s): Kathleen Feng, Kai Bartolone, Kartik Prabhu, Brianna McColm, Xingyu Ni, Siddhant Gupta, Priyanka Raina

Abstract:

Birch is a system-on-chip that efficiently and accurately accelerates the multi-task multi-domain extended reality (XR) perception pipeline, with workloads such as visual inertial odometry (VIO), eye gaze tracking, and scene understanding. Birch features vision modules with cascaded line buffers, in-step feature sorting, and double-buffered optical flow to extract and track …

Published in: IEEE Solid-State Circuits Letters
Page(s): 1 – 1
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3673168
Publisher: IEEE

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An Energy-Efficient CNN Processor Supporting Bi-Directional FPN for Small-Object Detection on High-Resolution Videos in 16-nm FinFET

An Energy-Efficient CNN Processor Supporting Bi-Directional FPN for Small-Object Detection on High-Resolution Videos in 16-nm FinFET 150 150
Author(s): Yu-Chun Ding, Chia-Yu Chang, Hui-Yun Tsai, Chun-Yeh Lin, Hao-Jiun Tu, Yu-Ching Su, Kai-Feng Chang, Tsung-Han Hsieh, Wen-Ching Chen, Nian-Shyang Chang, Chun-Pin Lin, Chi-Shi Chen, Chao-Tsung Huang

Abstract:

The capability to detect small objects precisely in real time is essential for intelligent systems, particularly in advanced driver assistance systems (ADASs), as it ensures continuous awareness of distant obstacles for enhanced safety. However, achieving high detection precision for small objects requires high-resolution input inference on deep convolutional neural network (…

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3628249
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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