edge computing

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Probabilistic Computing With Neuromorphic Elements

Probabilistic Computing With Neuromorphic Elements 150 150
Author(s): Zonglong Li, Laurie E. Calvet

Abstract:

This article presents a compact and low-power approach to probabilistic inference based on time-domain analog computation. The proposed system implements a Bayesian classifier using neuromorphic circuit elements simulated in a 130-nm CMOS technology. Probabilities are encoded in the duty cycle of deterministic spiking signals generated by neuron circuits, while likelihood …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 167 – 175
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3712198
Publisher: IEEE

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TD-dAJC: A 100 MS/s 2 pJ/Pixel Time-Domain Weight and Integrating-MAC-Based Direct Analog to MJPEG Compression for Video Sensor Nodes

TD-dAJC: A 100 MS/s 2 pJ/Pixel Time-Domain Weight and Integrating-MAC-Based Direct Analog to MJPEG Compression for Video Sensor Nodes 150 150
Author(s): Harshit Naman, Gourab Barik, Yudhajit Ray, Shreyas Sen

Abstract:

Always-on, high-quality video sensors are increasingly required in wearables, surveillance, and Internet-of-Things (IoT) devices, where stringent energy constraints make efficient in-sensor compression necessary. These systems must support high-definition (HD) and 4 K video, yet conventional digital video compression pipelines consume significant power because high-speed analog-to-digital converters (ADCs) must digitize every pixel, …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 16
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3712196
Publisher: IEEE

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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): 45 – 55
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3676833
Publisher: IEEE

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A 14-nm Nonvolatile-Volatile-Fused Compute-In-Memory Macro Based on Logic-Compatible Flash for Plastic Neural Networks

A 14-nm Nonvolatile-Volatile-Fused Compute-In-Memory Macro Based on Logic-Compatible Flash for Plastic Neural Networks 150 150
Author(s): Weizeng Li, Linfang Wang, Zhidao Zhou, Zhi Li, Wang Ye, Junyu Zhu, Junzhe Shen, Bohan Wang, Hanghang Gao, Hongyang Hu, Jing Liu, Jinshan Yue, Jianguo Yang, Qing Luo, Qi Liu, Chunmeng Dou, Yang Chai, Ming Liu

Abstract:

Designing computing-in-memory (CIM) chips with synaptic plasticity can potentially support energy-efficient on-chip learning in edge devices for rapid local task adaptation. Its silicon implementation is challenging as it requires hybridizing nonvolatile and volatile memory (VM) and customized computational operations. In this work, we propose a plastic CIM (P-CIM) macro featuring: 1) …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3652940
Publisher: IEEE

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