Neurons

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A CMOS-MRAM Stochastic Neuron with Programmable Pulse-Encoded Activation Functions

A CMOS-MRAM Stochastic Neuron with Programmable Pulse-Encoded Activation Functions 150 150
Author(s): J. H. Quintino Palhares, J. Miquel, E. Garello, L. Hutin

Abstract:

Probabilistic computing and uncertainty-aware inference require hardware-efficient generation of stochastic activations. This paper presents a CMOS-MRAM stochastic neuron in which stochasticity is encoded into programming-pulse distributions rather than relying exclusively on the intrinsic stochastic switching dynamics of the MRAM device. The nonlinear switching boundary of the SOT-MRAM acts as a …

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.2026.3742167
Publisher: IEEE

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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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Verifica: Near-Memory Symbolic Interval Computing Formal Neural Network Verification Accelerator

Verifica: Near-Memory Symbolic Interval Computing Formal Neural Network Verification Accelerator 150 150
Author(s): Edward Jongyoon Choi, Byeongseon Choi, Jiho Chun, Minkyu Je, Bongjin Kim

Abstract:

Formal robustness verification of neural networks is essential for deploying machine learning (ML) systems in safety-critical applications. However, existing verification frameworks are predominantly software-based, incurring prohibitive computational costs and long runtimes, severely limiting scalability and practical deployment. Although symbolic interval analysis enables scalable over-approximate verification, its effectiveness is limited by …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3707324
Publisher: IEEE

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Antiferromagnetic Programmable Neuron: Structure, Training, and Pattern Recognition Applications

Antiferromagnetic Programmable Neuron: Structure, Training, and Pattern Recognition Applications 150 150
Author(s): I. Sotnyk, O. Prokopenko

Abstract:

Artificial neurons based on antiferromagnetic (AFM) spin Hall oscillators (SHOs) are promising elements for creating ultrafast, energy-efficient neuromorphic computing systems. These structures can generate picosecond spikes in response to dc and ac electric currents, thereby mimicking the reaction of biological neurons to an external stimulus. However, conventional AFM neurons have …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 9 – 17
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3633490
Publisher: IEEE

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A Scalable 1024-Channel Ultra-Low-Power Spike Sorting Chip With Event-Driven Detection and Spatial Clustering

A Scalable 1024-Channel Ultra-Low-Power Spike Sorting Chip With Event-Driven Detection and Spatial Clustering 150 150
Author(s): Arash Akhoundi, Pumiao Yan, Yawende Landbrug, Madeline Hays, Boris Murmann, E. J. Chichilnisky, Dante G. Muratore

Abstract:

This article presents a 1024-channel ultra-low-power spike sorting chip featuring event-driven spike detection and spatial clustering for large-scale neural recording. To address power and scalability constraints in brain–computer interfaces (BCIs), the design integrates a compressive analog-to-digital converter (ADC) with a two-stage spike detector that significantly reduces memory and processing …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 17
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3611139
Publisher: IEEE

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LIF Neuron Based on a Charge-Powered Ring Oscillator in Weak Inversion Achieving 201 fJ/SOP

LIF Neuron Based on a Charge-Powered Ring Oscillator in Weak Inversion Achieving 201 fJ/SOP 150 150
Author(s): Javier Granizo, Ruben Garvi, Ricardo Carrero, Luis Hernandez

Abstract:

This letter presents the experimental results of a leaky-integrate-and-fire neuron (LIF) neuron based on time-domain analog circuitry. This kind of neuron is the core of spiking neural network (SNN) used in edge applications. Edge applications require power-efficient neuron designs whose power consumption is extremely low when idle, and low when …

Published in: IEEE Solid-State Circuits Letters
Page(s): 249 – 252
Year of Publication: 2025
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2025.3603335
Publisher: IEEE

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A Passive and Scalable High-Order Neuromorphic Circuit Enabled by Mott Memristors

A Passive and Scalable High-Order Neuromorphic Circuit Enabled by Mott Memristors 150 150
Author(s): Zikang Lin, Xiaohui Wu, Shujing Zhao, Weihua Liu, Xin Li, Li Geng, Chuanyu Han

Abstract:

In this study, VO2 Mott memristors have been successfully fabricated, leading to the proposal of a passive and scalable high-order neural circuit. This circuit consists of two coupled VO2 Mott memristors, two resistors, and three capacitors. The proposed high-order neural circuit demonstrates 11 distinct firing behaviors similar to those of biological …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 60 – 66
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3573709
Publisher: IEEE

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