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A 6-bit Serializer With Integrated Manchester Encoder Using Flexible a-IGZO TFT Technology

A 6-bit Serializer With Integrated Manchester Encoder Using Flexible a-IGZO TFT Technology 150 150
Author(s): Suyash Shrivastava, Pydi Ganga Bahubalindruni

Abstract:

This article presents an experimental characterization of a novel 6-bit parallel-to-serial data converter integrated with a Manchester encoder. The system comprises an on-chip digital logic block that generates six mutually nonoverlapping control signals ( $\phi _{0-5}$ ) and three 120° phase-shifted clock signals to drive the switching transistors in the serializer and the …

Published in: IEEE Solid-State Circuits Letters
Page(s): 194 – 197
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3707465
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 paper 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): 1 – 1
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3712198
Publisher: IEEE

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MITTA: A Multi-Task Transformer Accelerator With Mixed Precision Structured Sparsity and Hierarchical Task-Adaptive Power Management

MITTA: A Multi-Task Transformer Accelerator With Mixed Precision Structured Sparsity and Hierarchical Task-Adaptive Power Management 150 150
Author(s): Qirui Zhang, Zichen Fan, Pierre Abillama, Sara Shoouri, Jungho Lee, Chien-Wei Tseng, Changwoo Lee, Wei Meng, Hun-Seok Kim, David Blaauw, Dennis Sylvester

Abstract:

This article presents MITTA, the first silicon-proven transformer accelerator optimized for multi-task inference across both natural language processing (NLP) and image processing domains. MITTA accelerates a task-sharing algorithm that minimizes sub-task computation by reusing both activations and weights from a shared base task, requiring only sparse delta computation for sub-tasks. …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3669815
Publisher: IEEE

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A 7-Level 18-Wire-State Trio-Signaling Transmitter for MIPI C-PHY 3.0 Interfaces

A 7-Level 18-Wire-State Trio-Signaling Transmitter for MIPI C-PHY 3.0 Interfaces 150 150
Author(s): Junyeong Lee, Myeongjin Oh, Kiwoon Kim, Jung-Hoon Chun

Abstract:

This letter presents a MIPI C-PHY v3.0 TX, which adopts trio-signaling using three wires per lane. Each line supports seven-level signaling, enabling 18 wire states to map 32-bit data into nine symbols, achieving 3.56 bits/symbol efficiency. Balanced coding maintains constant driver current, enhancing SSO noise immunity, and embedded clocking is achieved …

Published in: IEEE Solid-State Circuits Letters
Page(s): 101 – 104
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3672642
Publisher: IEEE

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A Machine Learning-Inspired PAM-4 Transceiver for Medium-Reach Wireline Links

A Machine Learning-Inspired PAM-4 Transceiver for Medium-Reach Wireline Links 150 150
Author(s): Ramin Javadi, Tejasvi Anand

Abstract:

This article presents an energy-efficient machine learning-inspired PAM-4 wireline transceiver that leverages data encoding at the transmitter (Tx) and feature extraction with classification at the receiver (Rx) to compensate for channel loss ranging from 13 to 26 dB, while maintaining the bit error rate (BER)<10-11. A new consecutive symbol-to-center (CSC) encoding …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3616072
Publisher: IEEE

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ROZK: An Energy-Efficient DNN Accelerator Based on Reconfigurable NoC and Local Zero-Skipping

ROZK: An Energy-Efficient DNN Accelerator Based on Reconfigurable NoC and Local Zero-Skipping 150 150
Author(s): Heetak Kim, Yunpyo Hong, Byungsoo Kim, Jongsun Park

Abstract:

Zero-skipping is a famous technique to improve the energy efficiency of deep neural network (DNN) accelerators. When the zero-skipping is realized with encoded data using lossless compression, irregular and unpredictable size of data due to inconsistent compression rate incurs several design issues including: 1) load imbalance from irregularity of data stored …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 12
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3604814
Publisher: IEEE

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