Measurement units

  • No Comments

A Sensing-Security Co-Design Based on Intrinsic Device Variations for Self-Authenticating Sensors

A Sensing-Security Co-Design Based on Intrinsic Device Variations for Self-Authenticating Sensors 150 150
Author(s): Ruochen Wang, Tetsuya Hirose, Jun Shiomi, Yoshihiro Midoh, Takuji Miki, Makoto Nagata, Noriyuki Miura

Abstract:

This article presents an extended analog signature framework (ASF) that realizes sensing-security co-design by embedding authentication directly within the sensor analog front-end. Building on prior work, the extended design addresses entropy stabilization and structural periodicity through refined selection and measurement-bound feedback. Intrinsic transistor-level mismatches are reused as resources for authenticating, …

Published in: IEEE Solid-State Circuits Letters
Page(s): 246 – 249
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3725256
Publisher: IEEE

View on IEEE Xplore

  • No Comments

A Nesting-Connected Reconfigurable SC Converter With Fine-Grained Conversion Ratios and Increased Output Conduction Paths for On-Chip Surround Power Delivery

A Nesting-Connected Reconfigurable SC Converter With Fine-Grained Conversion Ratios and Increased Output Conduction Paths for On-Chip Surround Power Delivery 150 150
Author(s): Xuchu Mu, Yang Jiang, Xin Ming, Bo Zhang, Rui P. Martins, Pui-In Mak

Abstract:

This article presents a reconfigurable switched-capacitor (SC) DC–DC converter that achieves improved overall efficiency over a fine-grained conversion-ratio range, applicable to on-chip surround power delivery (SPD) in compact and lightweight devices. We propose a nesting-connected reconfigurable SC topology generation method, attaining fine-grained subdivision of reconfigurable voltage conversion ratios (VCRs) …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3702012
Publisher: IEEE

View on IEEE Xplore

  • No Comments

STAR-SRAM: 16-bit Floating-Point SRAM-Based Digital Computing-in-Memory Macro in a 28 nm

STAR-SRAM: 16-bit Floating-Point SRAM-Based Digital Computing-in-Memory Macro in a 28 nm 150 150
Author(s): Chuan-Tung Lin, Jonghyun Oh, Mingoo Seok

Abstract:

A digital computing-in-memory (DCIM) macro emerges as a promising building block in a deep neural network (DNN) accelerator. To better support DNN workloads, circuit designers aim to improve three main metrics for macros: energy efficiency, compute density, and weight density. Improvements in those metrics directly translate into reduced energy consumption, …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3694927
Publisher: IEEE

View on IEEE Xplore

  • No Comments

ASAP: A 28-nm Transformer Training Accelerator With Alternating Sparsity and Asymmetrical Microscaling Precision

ASAP: A 28-nm Transformer Training Accelerator With Alternating Sparsity and Asymmetrical Microscaling Precision 150 150
Author(s): HanGyeol Mun, Jian Meng, Xiaofeng Hu, Yuan Liao, Chun-Ting Chen, Jae-sun Seo

Abstract:

This work presents ASAP, a 28-nm transformer-training accelerator that combines N:M structured sparsity with asymmetric microscaling floating-point (MXFP) precision through a unified algorithm–hardware co-design. ASAP introduces a progressive sparsity schedule in which pruned compute resources are reassigned to increase numerical precision for important weights and activations, stabilizing optimization …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3693653
Publisher: IEEE

View on IEEE Xplore

Search this website