Frontiers in Electronics | Integrated Circuits and VLSI section | New and Recent Articles https://www.frontiersin.org/journals/electronics/sections/integrated-circuits-and-vlsi RSS Feed for Integrated Circuits and VLSI section in the Frontiers in Electronics journal | New and Recent Articles en-us Frontiers Feed Generator,version:1 2026-05-13T15:23:41.545+00:00 60 https://www.frontiersin.org/articles/10.3389/felec.2026.1743265 https://www.frontiersin.org/articles/10.3389/felec.2026.1743265 <![CDATA[Adiabatic capacitive neuron: an energy-efficient functional unit for artificial neural networks]]> 2026-02-24T00:00:00Z Original Research Sachin MaheshwariMike SmartHimadri Singh RaghavThemis ProdromakisAlexander Serb https://www.frontiersin.org/articles/10.3389/felec.2025.1645594 https://www.frontiersin.org/articles/10.3389/felec.2025.1645594 <![CDATA[An overview of advanced instruments for magnetic characterization and measurements]]> 2025-09-01T00:00:00Z Review Junbiao ZhaoLigang BaiShen LiZhiqiang CaoYi PengJinrui BaiXudong CaiXinmin ShiXiaoyang LinGuodong WeiXueying Zhang https://www.frontiersin.org/articles/10.3389/felec.2025.1469802 https://www.frontiersin.org/articles/10.3389/felec.2025.1469802 <![CDATA[Quantized convolutional neural networks: a hardware perspective]]> 2025-07-03T00:00:00Z Review Li ZhangOlga KrestinskayaMohammed E. FoudaAhmed M. EltawilKhaled Nabil Salama https://www.frontiersin.org/articles/10.3389/felec.2025.1608122 https://www.frontiersin.org/articles/10.3389/felec.2025.1608122 <![CDATA[Interface engineering induced Dzyaloshinskii-Moriya interaction enhancement in Py/Ti/CoFeB/MgO heterostructures]]> 2025-05-19T00:00:00Z Brief Research Report Xiaoyue SongZhiqiang CaoLigang BaiJunbiao ZhaoXudong CaiDapeng ZhuGuodong Wei https://www.frontiersin.org/articles/10.3389/felec.2025.1567562 https://www.frontiersin.org/articles/10.3389/felec.2025.1567562 <![CDATA[SPIKA: an energy-efficient time-domain hybrid CMOS-RRAM compute-in-memory macro]]> 2025-04-30T00:00:00Z Original Research Khaled HumoodYihan PanGrahame ReynoldsMohammed MughalShiwei WangAlexander SerbThemis Prodromakis https://www.frontiersin.org/articles/10.3389/felec.2025.1513127 https://www.frontiersin.org/articles/10.3389/felec.2025.1513127 <![CDATA[Energy-efficient analog-domain aggregator circuit for RRAM-based neural network accelerators]]> 2025-02-04T00:00:00Z Original Research Khaled HumoodYihan PanShiwei WangAlexander SerbThemis Prodromakis https://www.frontiersin.org/articles/10.3389/felec.2024.1377080 https://www.frontiersin.org/articles/10.3389/felec.2024.1377080 <![CDATA[Compact grounded memristor model with resistorless and tunability features]]> 2024-11-13T00:00:00Z Original Research Ankit MehtaArash AhmadiMajid Ahmadi https://www.frontiersin.org/articles/10.3389/felec.2024.1366299 https://www.frontiersin.org/articles/10.3389/felec.2024.1366299 <![CDATA[LIF neuron —a memristive realization]]> 2024-09-06T00:00:00Z Original Research Khalid AlammariMoslem HeidarpurMajid AhmadiArash Ahmadi https://www.frontiersin.org/articles/10.3389/felec.2024.1409548 https://www.frontiersin.org/articles/10.3389/felec.2024.1409548 <![CDATA[S-Tune: SOT-MTJ manufacturing parameters tuning for securing the next generation of computing]]> 2024-08-07T00:00:00Z Original Research Muhtasim Alam ChowdhuryMousam HossainChristopher MastrangeloRonald F. DeMaraSoheil Salehi https://www.frontiersin.org/articles/10.3389/felec.2023.1343612 https://www.frontiersin.org/articles/10.3389/felec.2023.1343612 <![CDATA[Hardware acceleration of DNA pattern matching using analog resistive CAMs]]> 2024-02-12T00:00:00Z Original Research Jinane BazziJana SweidanMohammed E. FoudaRouwaida KanjAhmed M. Eltawil https://www.frontiersin.org/articles/10.3389/felec.2023.1331280 https://www.frontiersin.org/articles/10.3389/felec.2023.1331280 <![CDATA[Demonstration of transfer learning using 14 nm technology analog ReRAM array]]> 2024-01-15T00:00:00Z Original Research Fabia Farlin AthenaOmobayode FagbohungbeNanbo GongMalte J. RaschJimmy PenalozaSoonCheon SeoArthur GasasiraPaul SolomonValeria BragagliaSteven ConsiglioHisashi HiguchiChanro ParkKevin BrewPaul JamisonChristopher CatanoIqbal SarafClaire SilvestreXuefeng LiuBabar KhanNikhil JainSteven McDermottRick JohnsonI. Estrada-RaygozaJuntao LiTayfun GokmenNing LiRuturaj PujariFabio CartaHiroyuki MiyazoeMartin M. FrankAntonio La PortaDevi KotyQingyun YangRobert D. ClarkKandabara TapilyCory WajdaAelan MosdenJeff ShearerAndrew MetzSean TeehanNicole SaulnierBert OffreinTakaaki TsunomuraGert LeusinkVijay NarayananTakashi Ando https://www.frontiersin.org/articles/10.3389/felec.2023.1129675 https://www.frontiersin.org/articles/10.3389/felec.2023.1129675 <![CDATA[Straightforward data transfer in a blockwise dataflow for an analog RRAM-based CIM system]]> 2023-04-17T00:00:00Z Methods Yuyi LiuBin GaoPeng YaoQi LiuQingtian ZhangDong WuJianshi TangHe QianHuaqiang Wu https://www.frontiersin.org/articles/10.3389/felec.2022.1091369 https://www.frontiersin.org/articles/10.3389/felec.2022.1091369 <![CDATA[CoFHE: Software and hardware Co-design for FHE-based machine learning as a service]]> 2023-01-12T00:00:00Z Original Research Mengxin ZhengLei JuLei Jiang 160 Watt, due to their huge capacity (e.g., 512 MB) on-chip SRAM scratchpad memories.Methods: In this paper, we propose a software and hardware co-designed FHE-based MLaaS framework, CoFHE. From the software perspective, we propose an FHE compiler to select the best FHE scheme for a network architecture. We also build a low-power and high-density NAND-SPIN and SRAM hybrid scratchpad memory system for FHE hardware accelerators.Results: On average, under the same security and accuracy constraints, on average, CoFHE accelerates various FHE-based inferences by 18%, and reduces the energy consumption of various FHE-based inferences by 26%.Discussion: CoFHE greatly improves the latency and energy efficiency of FHE-based MLaaS.]]> https://www.frontiersin.org/articles/10.3389/felec.2022.1032485 https://www.frontiersin.org/articles/10.3389/felec.2022.1032485 <![CDATA[XMA2: A crossbar-aware multi-task adaption framework via 2-tier masks]]> 2022-12-20T00:00:00Z Original Research Fan ZhangLi YangJian MengJae-sun SeoYu CaoDeliang Fan https://www.frontiersin.org/articles/10.3389/felec.2022.877629 https://www.frontiersin.org/articles/10.3389/felec.2022.877629 <![CDATA[Energy-efficient neural network design using memristive MAC unit]]> 2022-09-26T00:00:00Z Original Research Shengqi YuThanasin BunnamSirichai TriamlumlerdManoch PrachaFei XiaRishad ShafikAlex Yakovlev https://www.frontiersin.org/articles/10.3389/felec.2022.898273 https://www.frontiersin.org/articles/10.3389/felec.2022.898273 <![CDATA[AI-PiM—Extending the RISC-V processor with Processing-in-Memory functional units for AI inference at the edge of IoT]]> 2022-08-11T00:00:00Z Original Research Vaibhav VermaMircea R. Stan https://www.frontiersin.org/articles/10.3389/felec.2022.792326 https://www.frontiersin.org/articles/10.3389/felec.2022.792326 <![CDATA[Design and Analysis of a Resistive Sensor Interface With Phase Noise-Energy-Resolution Scalability for a Time-Based Resistance-to-Digital Converter]]> 2022-04-25T00:00:00Z Original Research Dong-Hyun SeoBaibhab ChatterjeeSean M. ScottDaniel J. ValentinoDimitrios PeroulisShreyas Sen https://www.frontiersin.org/articles/10.3389/felec.2022.856284 https://www.frontiersin.org/articles/10.3389/felec.2022.856284 <![CDATA[Statistical Analysis Based Feature Selection Enhanced RF-PUF With >99.8% Accuracy on Unmodified Commodity Transmitters for IoT Physical Security]]> 2022-04-25T00:00:00Z Original Research Md Faizul Bari Parv Agrawal Baibhab Chatterjee Shreyas Sen  99.8% accuracy with a network of higher model capacity, for the first time in literature without any assisting digital preamble. The design space has been explored in detail and the effect of the wireless channel has been investigated. The performance of some popular machine learning algorithms has been tested and compared with the neural network approach. A thorough investigation of various PUF properties has been done. With extensive testing of 41238000 cases, the detection probability for RF-PUF for our data is found to be 0.9987, which, for the first time, experimentally establishes RF-PUF as a strong authentication method. Finally, the potential attack models and the robustness of RF-PUF against them have been discussed.]]> https://www.frontiersin.org/articles/10.3389/felec.2022.847069 https://www.frontiersin.org/articles/10.3389/felec.2022.847069 <![CDATA[Hardware-Software Co-Design of an In-Memory Transformer Network Accelerator]]> 2022-04-11T00:00:00Z Original Research Ann Franchesca LagunaMohammed Mehdi SharifiArman KazemiXunzhao YinMichael NiemierX. Sharon Hu https://www.frontiersin.org/articles/10.3389/felec.2022.834146 https://www.frontiersin.org/articles/10.3389/felec.2022.834146 <![CDATA[CIDAN-XE: Computing in DRAM with Artificial Neurons]]> 2022-02-18T00:00:00Z Original Research Gian SinghAnkit WagleSunil KhatriSarma Vrudhula