Hardware

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A Mixed-Signal SAT Solver for Exploring Nonideal Effects and Scalability in Analog Computation

A Mixed-Signal SAT Solver for Exploring Nonideal Effects and Scalability in Analog Computation 150 150
Author(s): Ahmet Yusuf Salim, Soner Seçkiner, Eslam Elmitwalli, Zeljko Ignjatovic, Selçuk Köse

Abstract:

Recent years have seen growing interest in custom and nature-inspired computing methods for solving computationally challenging combinatorial optimization problems. The Boolean satisfiability (SAT), a canonical nondeterministic polynomial (NP)-complete problem, has remained a focal point due to its wide applicability in artificial intelligence, cryptography, and formal verification. This article presents …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 19
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3721413
Publisher: IEEE

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miniSQI: A Compact and Low-Power SQIsign-1D Post-Quantum Digital Signature Verification Accelerator

miniSQI: A Compact and Low-Power SQIsign-1D Post-Quantum Digital Signature Verification Accelerator 150 150
Author(s): Krishna Sai Tarun Ramapragada, Utsav Banerjee

Abstract:

We present the first ASIC demonstration of SQIsign, an isogeny-based NIST-candidate post-quantum digital signature scheme. Our optimized Montgomery modular multiplier circuit design enables 2× speedup and energy savings, while efficient memory organization provides 31% area savings. We implement SQIsign-1D-Uncompressed signature verification in a small form factor with low power consumption suitable …

Published in: IEEE Solid-State Circuits Letters
Page(s): 1 – 1
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3720807
Publisher: IEEE

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Leveraging a Passive MRAM Crossbar for Hardware-in-the-Loop and Continual learning

Leveraging a Passive MRAM Crossbar for Hardware-in-the-Loop and Continual learning 150 150
Author(s): M. Drouhin, M. Grelier, M. Deroo, Y. Sassi, E. Monteblanco, N. Davila, G. Mihajlović, R. V. Chopdekar, T. S. Santos, J. A. Katine, D. Ravelosona

Abstract:

Artificial neural networks (ANNs) have enabled major advances in artificial intelligence, yet their growing computational and energy demands challenge conventional von Neumann architectures due to the costly separation of memory and processing. In-memory computing has emerged as a promising solution, particularly through memristive crossbar arrays capable of performing multiply-and-accumulate operations …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 129 – 134
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3702001
Publisher: IEEE

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A 12 b 180-MS/s Pipelined-SAR ADC With a Low-Power Calibration-Free RA and Hardware-Efficient SAR Logic

A 12 b 180-MS/s Pipelined-SAR ADC With a Low-Power Calibration-Free RA and Hardware-Efficient SAR Logic 150 150
Author(s): Guo Wei, Rui Zhang, Fule Li, Hanjun Jiang, Zhihua Wang

Abstract:

This letter presents a 12-bit, 180-MS/s pipelined-SAR ADC in 65-nm CMOS. To eliminate the complex interstage gain-error calibration for a fast-response characteristic, a high-gain residue amplifier (RA) featuring a two-stage gain-boosting architecture is proposed. By removing the tail current, the RA significantly alleviates slew-rate and voltage headroom limitations. The …

Published in: IEEE Solid-State Circuits Letters
Page(s): 186 – 189
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3697220
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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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

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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

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A 11.0-TOPS/W Diffusion Accelerator With Temporal Data Reuse for Real-Time Text-to-Motion Generation

A 11.0-TOPS/W Diffusion Accelerator With Temporal Data Reuse for Real-Time Text-to-Motion Generation 150 150
Author(s): Sungwoong Yune, Jieon Yoon, Hangyeol Lee, Adiwena Putra, Jaehoon Heo, Joo-Young Kim

Abstract:

Text-to-motion models are AI systems that generate human motion sequences directly from natural language descriptions, serving as key enablers for immersive virtual avatars and interactive digital humans in AR/VR ecosystems. However, state-of-the-art text-to-motion diffusion models suffer from substantial computational costs due to their iterative nature, making them ill-suited for …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3689546
Publisher: IEEE

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Adelia: A 4-nm LLM Processing Unit With Streamlined Dataflow and Dual-Mode Parallelism for Maximizing Hardware Efficiency

Adelia: A 4-nm LLM Processing Unit With Streamlined Dataflow and Dual-Mode Parallelism for Maximizing Hardware Efficiency 150 150
Author(s): Sukbin Lim, Jung-Hoon Kim, Seungjae Moon, Junseo Cha, Dongjin Seo, Jongho Kim, Hunjong Lee, Jinwon Lee, Joo-Young Kim

Abstract:

The proliferation of large language models (LLMs) as cross-domain foundation models is fueled by aggressive scaling in both parameter counts and inference-time computation. The emergence of sophisticated reasoning models further accelerates this trend, demanding longer context windows and escalating the computational and memory burdens of inference. A fundamental challenge arises …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1513 – 1525
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3663603
Publisher: IEEE

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