Artificial intelligence

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EMO-CIM: An Input/Stationary-Data Similarity-Aware Computing-In-Memory Design for Variable Vector-Wise Computation in Edge Multioperator AI Acceleration

EMO-CIM: An Input/Stationary-Data Similarity-Aware Computing-In-Memory Design for Variable Vector-Wise Computation in Edge Multioperator AI Acceleration 150 150
Author(s): Bo Wang, Yuchen Tang, Feiran Liu, Xiaoxue Zhong, Yuchen Ma, Xin Si, Jun Yang

Abstract:

We propose an edge multioperator computing-in-memory (EMO-CIM) design that supports variable vector-wise multiply-and-accumulate (MAC) in CNN, Depthwise (DW)-Convolution, and Attention operators. It features: 1) a single EMO-CIM bank (ECB) excels in variable vector-wise MAC (V-MAC) for multioperators; 2) merging local input-shared compute units (LISCUs) with a decode-unit and adder-tree (DUAT) facilitates …

Published in: IEEE Solid-State Circuits Letters
Page(s): 97 – 100
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3668853
Publisher: IEEE

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A 56-Gb/s Hybrid Silicon Photonic and 5-nm CMOS 3-D-Integrated Transceiver for Optical Compute I/O

A 56-Gb/s Hybrid Silicon Photonic and 5-nm CMOS 3-D-Integrated Transceiver for Optical Compute I/O 150 150
Author(s): Ganesh Balamurugan, Parmanand Mishra, Subal Sahni, Ankur Aggarwal, Simon Forey, Andrew Gimlett, Prateek Goyal, Han Hao, Santosh Hariwan, Masum Hossain, Sejun Jeon, Narayan Kaniyur, David Lazovsky, Wonho Lee, Bengt Littmann, Raj Nagulapalli, Kevin Park, John Rollinson, Matteo Staffaroni, Prakash Thakur, Saurabh Vats, Preet Virk, Dehua Xiao, Hemesh Yasotharan, Raman Yazdani, Waleed Younis, Shifeng Yu, Phil Winterbottom

Abstract:

This work presents a hybrid 3-D-integrated silicon photonic (SiPh) transceiver suitable for realizing chiplet-based optical I/O in future AI/ML ASIC packages. The optical transceiver die stack is composed of two ICs: a SiPh IC (PIC) with micrometer-scale, thermally robust electro-absorption modulators (EAMs), and a 5-nm CMOS electronic IC (…

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 11
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3655295
Publisher: IEEE

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A 3-D HBI Compliant 1.536 TB/s/mm2 Bandwidth Scalable Attention Accelerator With 22.5-GOPS Throughput High Speed SoftMax for Quantized Transformers in Intel 3

A 3-D HBI Compliant 1.536 TB/s/mm2 Bandwidth Scalable Attention Accelerator With 22.5-GOPS Throughput High Speed SoftMax for Quantized Transformers in Intel 3 150 150
Author(s): Prerna Budhkar, Mirco Sciulli, Srivatsa Rangachar Srinivasa, Gauthaman Murali, Ragh Kuttappa, Paolo Aseron, Trang Nguyen, Vinayak Honkote, Tanay Karnik

Abstract:

This letter presents a novel hardware accelerator compatible with <3- $\mu $ m pitch 3-D Cu-Cu hybrid bonding interconnect (HBI) technology, particularly designed to efficiently execute multihead attention (MHA) of encoder transformer models. We present an accelerator that addresses performance losses due to low precision models by incorporating specialized hardware optimizations …

Published in: IEEE Solid-State Circuits Letters
Page(s): 69 – 72
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3659575
Publisher: IEEE

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SparseCol: A 1320 BTOPS/W Precision-Scalable NPU Exploiting Training-Free Structured Bit-Level Sparsity and Dynamic Dataflow

SparseCol: A 1320 BTOPS/W Precision-Scalable NPU Exploiting Training-Free Structured Bit-Level Sparsity and Dynamic Dataflow 150 150
Author(s): Man Shi, Vikram Jain, Weijie Jiang, Chao Fang, Antony Joseph, Wim Dehaene, Marian Verhelst

Abstract:

Bit-serial computation enables sequential processing of data at the bit level, providing several advantages, such as scalable computational precision. This approach has gained significant attention, especially for exploiting bit-level sparsity (BLS) in AI workloads. While current bit-serial processors leverage BLS to eliminate the computation associated with zero bits, they face …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3636451
Publisher: IEEE

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A 112-Gb/s PAM4 Receiver With a Phase Equalization AFE in 7-nm FinFET

A 112-Gb/s PAM4 Receiver With a Phase Equalization AFE in 7-nm FinFET 150 150
Author(s): Huanan Guo, Yufeng Yao, Xiang Gao

Abstract:

To reduce the bit-error-rate (BER), equalizers are implemented in high-speed SerDes receivers (RX) to compensate for channel insertion loss and mitigate intersymbol interference (ISI). Conventional analog front-end (AFE) designs primarily focus on amplitude gain while neglecting the influence of phase shift. This brief presents a phase equalization (PEQ) AFE design …

Published in: IEEE Solid-State Circuits Letters
Page(s): 5 – 8
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2025.3637833
Publisher: IEEE

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MEGA.mini: An Energy-Efficient NPU Leveraging a Novel Big/Little Core With Hybrid Input Activation for Generative AI Acceleration

MEGA.mini: An Energy-Efficient NPU Leveraging a Novel Big/Little Core With Hybrid Input Activation for Generative AI Acceleration 150 150
Author(s): Donghyeon Han, Anantha P. Chandrakasan

Abstract:

This article presents a processor for the acceleration of generative AI (GenAI) based on a novel heterogeneous core architecture called MEGA.mini. The processor introduces three algorithmic features: 1) fixed-point (FXP) and floating-point (FP) hybrid input activation (IA) representation; 2) a delayed-statistics-based normalization (NORM); and 3) conditional polynomial-based nonlinear activation (NLA) approximation. These …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 14
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3626894
Publisher: IEEE

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A 0.8-μm 32-Mpixel Always-On CMOS Image Sensor With Windmill-Pattern Edge Extraction and On-Chip DNN

A 0.8-μm 32-Mpixel Always-On CMOS Image Sensor With Windmill-Pattern Edge Extraction and On-Chip DNN 150 150
Author(s): Mamoru Sato, Sachio Akebono, Kazuyoshi Yasuoka, Eriko Kato, Masahiro Tsuruta, Chiaki Takano, Kensuke Ota, Kazuki Haraguchi, Masahiro Watanabe, Genki Fujii, Koichiro Yamanaka, Kazunori Yasuda, Satoshi Minami, Katsuhiko Hanzawa, Kohei Matsuda, Akihiko Kato, Yosuke Ueno

Abstract:

This letter presents a CMOS image sensor (CIS) that integrates two operation modes: 1) a high-resolution viewing mode with $0.8~\mu $ m 32 Mpixels and 2) a low-power always-on object recognition mode consuming 2.67 mW at 10 frames/s. The CIS features a unique windmill-pattern analog edge extraction circuit that is resilient to illumination variations. An …

Published in: IEEE Solid-State Circuits Letters
Page(s): 353 – 356
Year of Publication: 2025
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2025.3628314
Publisher: IEEE

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DPIM: A 2T1C eDRAM Transformer-in-Memory Chip With Sparsity-Aware Quantization and Heterogeneous Dense–Sparse Core

DPIM: A 2T1C eDRAM Transformer-in-Memory Chip With Sparsity-Aware Quantization and Heterogeneous Dense–Sparse Core 150 150
Author(s): Donghyuk Kim, Jae-Young Kim, Hyunjun Cho, Seungjae Yoo, Sukjin Lee, Sungwoong Yune, Sejeong Yang, Hoichang Jeong, Keonhee Park, Ki-Soo Lee, Jongchan Lee, Chanheum Han, Gunmo Koo, Yuli Han, Jaejin Kim, Jaemin Kim, Kyuho Jason Lee, Joo-Hyung Chae, Kunhee Cho, Joo-Young Kim

Abstract:

Transformer models have revolutionized artificial intelligence (AI) applications across various domains, but their increasing complexity poses significant challenges in terms of computational and memory demands. While processing-in-memory (PIM) paradigms have been adopted to address these limitations, existing PIM-based transformer accelerators still face hurdles such as: 1) focusing solely on optimizing attention …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 16
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3607826
Publisher: IEEE

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A 28-nm Computing-in-Memory Processor With Zig-Zag Backbone-Systolic CIM and Block-/Self-Gating CAM for NN/Recommendation Applications

A 28-nm Computing-in-Memory Processor With Zig-Zag Backbone-Systolic CIM and Block-/Self-Gating CAM for NN/Recommendation Applications 150 150
Author(s): Shengzhe Yan, Zhuoyu Dai, Zhaori Cong, Zeyu Guo, Yifan He, Wenyu Sun, Chunmeng Dou, Feng Zhang, Jinshan Yue, Yongpan Liu, Ming Liu

Abstract:

Computing-in-memory (CIM) chips have demonstrated promising energy efficiency for artificial intelligence (AI) applications such as neural networks (NNs), Transformer, and recommendation system (RecSys). However, several challenges still exist. First, a large gap between the macro and system-level CIM energy efficiency is observed. Second, several memory-dominate operations, such as embedding in …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 15
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3610539
Publisher: IEEE

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