transformer

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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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A “No Gain” Direct-Conversion IQ RF-to-Bits Receiver Without Active Linear Amplification

A “No Gain” Direct-Conversion IQ RF-to-Bits Receiver Without Active Linear Amplification 150 150
Author(s): Jeroen Ponte, Roel Plompen, Emiel Zijlma, Eric A. M. Klumperink, Harijot Singh Bindra, Bram Nauta

Abstract:

This work describes a direct-conversion IQ receiver (RX) that does not utilize any active linear (power) amplification, covering its design considerations, prototype implementation, and measurement verification. Only RLC components, MOS transistor (MOST) switches, and comparators are used, leading to several unique design challenges. Key among these are the fact that …

Published in: IEEE Journal of Solid-State Circuits
Page(s): 1 – 17
Year of Publication: 2025
Electronic ISSN: 1558-173X
DOI: 10.1109/JSSC.2025.3611114
Publisher: IEEE

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Integrating Atomistic Insights With Circuit Simulations via Transformer-Driven Symbolic Regression

Integrating Atomistic Insights With Circuit Simulations via Transformer-Driven Symbolic Regression 150 150
Author(s): Md Rahatul Islam Udoy, Jack Hutchins, Shamiul Alam, Catherine Schuman, Ahmedullah Aziz

Abstract:

This article introduces a framework that establishes a cohesive link between the first principles-based simulations and circuit-level analyses using a machine learning-based compact modeling platform. Starting with atomistic simulations, the framework examines the microscopic details of material behavior, forming the foundation for later stages. The generated datasets, with molecular insights, …

Published in: IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Page(s): 179 – 187
Year of Publication: 2025
Electronic ISSN: 2329-9231
DOI: 10.1109/JXCDC.2025.3624662
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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