Computer architecture

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A Multicore Programmable Variable-Precision Near-Memory Accelerator for CNN and Transformer Models

A Multicore Programmable Variable-Precision Near-Memory Accelerator for CNN and Transformer Models 150 150
Author(s): Yiming Yang, Yiyang Yuan, Xinghua Wang, Xiaoran Li, Hao Wu, Qihao Liu, Weiye Tang, Xiangqu Fu, Feng Zhang

Abstract:

Convolutional neural network (CNN) and transformer are the most popular neural network models in computer vision (CV) and natural language processing (NLP). It is quite common to use both these two models in multimodal scenarios, such as text-to-image generation. However, these two models have very different memory mappings, dataflows and …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3624011
Publisher: IEEE

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PANNA: A 558 TOPS/W Pipelined All-Analog Neural Network Accelerator in 22 nm FD-SOI

PANNA: A 558 TOPS/W Pipelined All-Analog Neural Network Accelerator in 22 nm FD-SOI 150 150
Author(s): Jakob Finkbeiner, Raphael Nägele, Manuel Wittlinger, Markus Grözing, Manfred Berroth, Georg Rademacher

Abstract:

Analog computing offers intrinsic energy and latency benefits that makes it attractive for real-time and edge applications. Conventional analog accelerators suffer from repeated conversions between analog and digital domain, which degrades efficiency and throughput. We propose an all-analog pipelined neural network accelerator architecture in 22 nm fully-depleted silicon-on-insulator (FD-SOI) complementary metal-oxide-semiconductor (…

Published in: IEEE Solid-State Circuits Letters
Page(s): 365 – 368
Year of Publication: 2025
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2025.3627274
Publisher: IEEE

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AACIM: A 2785-TOPS/W, 161-TOP/mm2, <1.17%-RMSE, Analog-In Analog-Out Computing-In-Memory Macro in 28 nm

AACIM: A 2785-TOPS/W, 161-TOP/mm2, <1.17%-RMSE, Analog-In Analog-Out Computing-In-Memory Macro in 28 nm 150 150
Author(s): Rentao Wan, Chuan-Tung Lin, Bo Zhang, Yannis Tsividis, Mingoo Seok

Abstract:

This article presents an analog-in analog-out CIM macro (AACIM) for use in analog deep neural network (DNN) processors. Our macro receives analog inputs, performs a 64-by-32 vector–matrix multiplication (VMM) with a current-discharging computation mechanism, and produces analog outputs. It stores a 4-bit weight as an analog voltage in the …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 11
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3622309
Publisher: IEEE

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Impact of Aging, Self-Heating, and Parasitics Effects on NSFET and CFET

Impact of Aging, Self-Heating, and Parasitics Effects on NSFET and CFET 150 150
Author(s): Swati Deshwal, Sufia Shahin, Anirban Kar, Yogesh S. Chauhan, Hussam Amrouch

Abstract:

This work presents a comparative analysis of complementary field-effect transistor (CFET) and nanosheet FET (NSFET) architectures, with a focus on self-heating effects (SHEs), negative bias temperature instability (NBTI), hot carrier degradation (HCD), and the impact of back-end-of-line (BEOL) parasitics on standard cell performance. NBTI degradation is modeled using a framework …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 188 – 196
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3624653
Publisher: IEEE

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A Microscaling Multi-Mode Gain-Cell Computing-in-Memory Macro for Advanced AI Edge Device

A Microscaling Multi-Mode Gain-Cell Computing-in-Memory Macro for Advanced AI Edge Device 150 150
Author(s): Jen-Chun Tien, Ping-Chun Wu, Win-San Khwa, Ashwin Sanjay Lele, Jian-Wei Su, Chiao-Yen Cheng, Jun-Ming Hsu, Yu-Chen Chen, Le-Jung Hsieh, Jyun-Cheng Bai, Yu-Sheng Kao, Tsung-Han Lou, Jui-Jen Wu, Chung-Chuan Lo, Ren-Shuo Liu, Chih-Cheng Hsieh, Kea-Tiong Tang, Meng-Fan Chang

Abstract:

The microscaling (MX) format is an emerging data representation that quantizes high-bitwidth floating-point (FP) values into low-bitwidth FP-like values with a shared-scale (SS) exponent. When implemented with computing-in-memory (CIM), MX allows an attractive tradeoff between accuracy and hardware efficiency for specific neural network (NN) workloads. This work presents the first …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3617944
Publisher: IEEE

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A 168 nW to 44.3 Mb/s Adaptable TRNG With 400 mV Attack-Resilient Hybrid RO Core

A 168 nW to 44.3 Mb/s Adaptable TRNG With 400 mV Attack-Resilient Hybrid RO Core 150 150
Author(s): Berkay Özbek, Timothy G. Constandinou

Abstract:

This letter presents an adaptable ring oscillator (RO)-true random number generator (TRNG) that removes the fixed power–throughput tradeoff by selecting delay-cell physics at run time. A hybrid core uses a current-starved inverter in low-power (LP) mode to amplify slew-limited jitter for high bit-efficiency at low frequency, and a …

Published in: IEEE Solid-State Circuits Letters
Page(s): 325 – 328
Year of Publication: 2025
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2025.3621413
Publisher: IEEE

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Understanding Reliability Trade-Offs in 1T-nC and 2T-nC FeRAM Designs

Understanding Reliability Trade-Offs in 1T-nC and 2T-nC FeRAM Designs 150 150
Author(s): Sadik Yasir Tauki, Rudra Biswas, Rakesh Acharya, Jiahui Duan, Rajiv Joshi, Kai Ni, Vijaykrishnan Narayanan

Abstract:

Ferroelectric random access memory (FeRAM) is a promising candidate for energy-efficient nonvolatile memory, particularly for logic-in-memory and compute-in-memory (CIM) applications. Among the available cell architectures, One-Transistor–n-Capacitor (1T-nC) and two-transistor–n-capacitor (2T-nC) FeRAMs each offer distinct trade-offs in density, scalability, and reliability. In this work, we present a comparative study …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 148 – 156
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3619908
Publisher: IEEE

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A 28-Gb/mm2 4XX-Layer 1-Tb 3-b/Cell WF-Bonding 3D-nand Flash With 5.6-Gb/s/Pin IOs

A 28-Gb/mm2 4XX-Layer 1-Tb 3-b/Cell WF-Bonding 3D-nand Flash With 5.6-Gb/s/Pin IOs 150 150
Author(s): Sang-Soo Park, Jae-Doeg Lyu, Myungjun Kim, Jaeyun Lee, Youngsun Song, Chung-Ho Yu, Hirano Makoto, Yongseok Kwon, Jong-Hoon Park, Ho-Joon Kim, Daein Lee, Donghyun Seo, Philkyu Kang, Byungrok Go, Minseok Kim, Seoyoon Jeon, Jaehwan Ryu, Jungmin Seo, Hwan-Seok Ku, Youngmin Jo, Doo-Hyun Kim, Hyunjun Yoon, Wontae Kim, Inmo Kim, Sunghoon Kim, Jiho Cho, Moosung Kim, Jae-Woo Im, Jongmin Park, Youngdon Choi, Il-Han Park, Chiweon Yoon, Hyunsuk Kim, Jaehoon Jang, Seungjae Lee, Ki-Whan Song, Sung-Hoi Hur

Abstract:

The challenge of evolving to create a memory that is shrinking compared to the previous generation while satisfying the high performance and low power required for flash memory has been present in every generation, but the recent rapid change to artificial intelligence (AI) trends is very tough, as the level …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 9
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3613774
Publisher: IEEE

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Beyond Backside Power: Backside Signal Routing as Technology Booster for Standard-Cell Scaling

Beyond Backside Power: Backside Signal Routing as Technology Booster for Standard-Cell Scaling 150 150
Author(s): Anup Ashok Kedilaya, Sirish Oruganti, Nishant Gupta, Xiuhao Zhang, Ilya Karpov, Mark A. Anders, Jaydeep P. Kulkarni

Abstract:

Advances in process technology enabling backside metals (BSMs) and contacts offer new design–technology co-optimization (DTCO) opportunities to further enhance power, performance, and area gains (PPA) in sub-3-nm nodes. This work exploits backside (BS) contact technology within standard cells to extend both signal and clock routing to BSM layers, …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 107 – 115
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3617784
Publisher: IEEE

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