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A CMOS Probabilistic Computing Chip With Hardware-Aware Learning

A CMOS Probabilistic Computing Chip With Hardware-Aware Learning 150 150
Author(s): Jinesh Jhonsa, William Whitehead, Yihao Wu, David Mccarthy, Shuvro Chowdhury, Kerem Y. Camsari, Luke Theogarajan

Abstract:

This work demonstrates a compact probabilistic computing system based on a physics-inspired probabilistic bit (p-bit) architecture with 440 interacting spins configured in a chimera graph and occupying 0.44 mm2 of silicon area. Area efficiency is achieved through a current-mode neuron update circuit and a mixed-signal design approach that integrates pitch-matched standard-cell analog …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 86 – 94
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3689506
Publisher: IEEE

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A 3 nm FinFET 125 TOPS/W-29 TFLOPS/W, 90 TOPS/mm2-17 TFLOPS/mm2 SRAM-Based INT8, and FP16 Digital-CIM Compiler With Support for Multi-Weight Update/Cycle

A 3 nm FinFET 125 TOPS/W-29 TFLOPS/W, 90 TOPS/mm2-17 TFLOPS/mm2 SRAM-Based INT8, and FP16 Digital-CIM Compiler With Support for Multi-Weight Update/Cycle 150 150
Author(s): Haruki Mori, Je-Min Hung, Wei-Chang Zhao, Kinshuk Khare, Brian Crafton, Hiroaki Ishii, Cheng-En Lee, Xiaochen Peng, Xiaoyu Sun, Yorinobu Fujino, Chia-Jung Tsen, Vineet Joshi, Chao-Kai Chuang, Takeshi Hashizume, Chia-Fu Lee, Tan-Li Chou, Kerem Akarvardar, Saman Adham, Yih Wang, Hidehiro Fujiwara, Yu-Der Chih, Yen-Huei Chen, Hung-Jen Liao, Tsung-Yung Jonathan Chang

Abstract:

This article presents an static random-access memory (SRAM)-based digital compute-in-memory (CIM) compiler implemented with 3 nm high- $\kappa $ metal gate (HKMG) FinFET technology, supporting flexible INT8 and FP16 formats for weight and activation multiply-accumulate (MAC) operations, offering configuration flexibility, high accuracy, and improved area and power efficiency. The FP16 digital …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1502 – 1512
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3646072
Publisher: IEEE

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OISMA: On-the-Fly In-Memory Stochastic Multiplication Architecture for Approximate Matrix Multiplication

OISMA: On-the-Fly In-Memory Stochastic Multiplication Architecture for Approximate Matrix Multiplication 150 150
Author(s): Shady Agwa, Yihan Pan, Georgios Papandroulidakis, Themis Prodromakis

Abstract:

Artificial intelligence (AI) models are currently driven by a significant upscaling of their complexity, with massive matrix-multiplication workloads representing the major computational bottleneck. In-memory computing (IMC) architectures are proposed to avoid the von Neumann bottleneck. However, both digital/binary-based and analog IMC architectures suffer from various limitations, which significantly degrade …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 56 – 64
Year of Publication: 2026
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2026.3680281
Publisher: IEEE

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Denim: Heterogeneous Compute-in-Memory Accelerator Exploiting Denoising–Similarity for Diffusion Models

Denim: Heterogeneous Compute-in-Memory Accelerator Exploiting Denoising–Similarity for Diffusion Models 150 150
Author(s): Ruiqi Guo, Lei Wang, Xiaofeng Chen, Keyi Jiang, Zhiheng Yue, Huiming Han, Yang Wang, Fengbin Tu, Shaojun Wei, Yang Hu, Shouyi Yin

Abstract:

Diffusion models have recently revolutionized the field of image synthesis due to their ability to generate photorealistic images. However, one of the main drawbacks of diffusion models is that the image generation process is expensive. Large image-to-image networks have to be applied multiple times in order to iteratively optimize the …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 15
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3684745
Publisher: IEEE

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HyFPCiM: A 65-nm 417-μW Error-Sensitivity-Aware FP8 Compute-in-Memory Macro

HyFPCiM: A 65-nm 417-μW Error-Sensitivity-Aware FP8 Compute-in-Memory Macro 150 150
Author(s): Gaurav Kumar K, Yatharth Agarwal, Kaushik Roy

Abstract:

This letter presents HyFPCiM, a 65-nm FP8 compute-in-memory (CiM) macro that enables sub-mW floating-point (FP) inference using error-sensitivity-aware FP partitioning (EAP). EAP maps exponent processing to a digital CiM (DCiM) path and mantissa accumulation to an analog CiM (ACiM), avoiding the power- and area-intensive adder-tree-based accumulation used in prior FP-CiM …

Published in: IEEE Solid-State Circuits Letters
Page(s): 146 – 149
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3688988
Publisher: IEEE

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A Time-Domain CNN Engine With Adaptive-Precision Computing and Threshold-Controllable Prediction for Edge Computing

A Time-Domain CNN Engine With Adaptive-Precision Computing and Threshold-Controllable Prediction for Edge Computing 150 150
Author(s): Aoming Zhan, Ye Zhao, Yulin Zhang, Gengchen Han, Qihang Jiang, Long Chen, Yumei Zhou, Shushan Qiao

Abstract:

With the growing demand for energy-efficient convolutional neural network (CNN) accelerators in edge intelligence, conventional digital CNN processors with fixed precision incur excessive switching energy and limited scalability. This work presents a time-domain CNN (TD-CNN) engine that achieves adaptive precision and computation reduction for ultralow-power operation. The main features include: 1) …

Published in: IEEE Solid-State Circuits Letters
Page(s): 142 – 145
Year of Publication: 2026
Electronic ISSN: 2573-9603
DOI: 10.1109/LSSC.2026.3687395
Publisher: IEEE

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A 28-nm System-in-One-Macro Computing-in-Memory Chip Utilizing Leakage-Eliminated 2T1C and Capacitor-Over-Logic 1T1C eDRAM

A 28-nm System-in-One-Macro Computing-in-Memory Chip Utilizing Leakage-Eliminated 2T1C and Capacitor-Over-Logic 1T1C eDRAM 150 150
Author(s): Zhihang Qian, Zhaori Cong, Shengzhe Yan, Zeyu Guo, Zhuoyu Dai, Yifan He, Wenyu Sun, Chunmeng Dou, Feng Zhang, Di Geng, Jinshan Yue, Yongpan Liu, Qi Liu, Ming Liu

Abstract:

Computing-in-memory (CIM) is a promising paradigm for energy- and area-efficient implementation of the heavy general matrix multiplication (GEMM) operations, especially in the evolving deep learning algorithms. Though existing CIM macros have demonstrated remarkable energy/area efficiency, the corresponding metrics of the system-level CIM chips degrade due to the peripheral components, …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 15
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3684971
Publisher: IEEE

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A 28-nm Digital Transpose SRAM Compute-in-Memory Macro With Accurate/Approximate Dual Mode for Floating-Point Edge Training and Inference

A 28-nm Digital Transpose SRAM Compute-in-Memory Macro With Accurate/Approximate Dual Mode for Floating-Point Edge Training and Inference 150 150
Author(s): Yiyang Yuan, Bingxin Zhang, Yiming Yang, Yishan Luo, Qirui Chen, Haitao Wang, Qihao Liu, Zhiming Chen, Hao Wu, Jinshan Yue, Shidong Lv, Xinghua Wang, Pui-In Mak, Xiaoran Li, Feng Zhang

Abstract:

Static random-access memory (SRAM)-based computing-in-memory (CIM) macros have been widely studied to improve the energy efficiency of edge artificial intelligence (AI) inference tasks. However, less attention has been given to AI training, which requires CIM macros to not only perform matrix multiply-accumulate (MAC) operations but also support matrix transposition. …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 13
Year of Publication: 2026
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2026.3679560
Publisher: IEEE

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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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