{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:58:30Z","timestamp":1762610310720,"version":"build-2065373602"},"reference-count":80,"publisher":"Association for Computing Machinery (ACM)","issue":"1","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Des. Autom. Electron. Syst."],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    In this work, we propose\n                    <jats:sc>InterAxNN<\/jats:sc>\n                    , an energy-aware approximate hardware architecture to perform vector-matrix multiplications in the binary precision regime for energy-constrained intermittently powered systems (IPS). In contrast to existing XNOR multiply-and-accumulate (MAC) operations implemented widely for binary neural networks (BNNs), we design a novel reconfigurable\n                    <jats:sc>XNOR-MAC<\/jats:sc>\n                    and\n                    <jats:sc>AND-MAC<\/jats:sc>\n                    memory macro to perform approximate binary precision operations, targeted for systems with extreme energy constraints. The proposed macro design is integrated with the ability to modify the MAC mode during run-time depending on instantaneous energy and power transients. We utilize the unique attributes of ferroelectric transistors (FeFETs) to implement the proposed ultra-low power BNN engine performing in-memory computing for artificial intelligence (AI) workloads. Subsequently, we leverage the quality configurable compute-in-memory-based hardware accelerator to implement\n                    <jats:sc>InterAxNN<\/jats:sc>\n                    based on a TI MSP430-based microcontroller. We evaluate the proposed\n                    <jats:sc>InterAxNN<\/jats:sc>\n                    concerning two baselines: (a) standard von Neumann computing architecture-based-microcontroller platform (MCU), and (b) MCU with a state-of-the-art low energy accelerator (MCU+LEA), and observe significant performance and energy benefits. Experimental results performed using a TI MSP430FR5379 IPS system show 448\u00d7\u2013581\u00d7 uplift in forward progress for 2%\u20138% accuracy loss for MNIST, 4%\u20135% accuracy loss for EMNIST, and 1%\u20134% reduction in accuracy for QMNIST, respectively, using MLP on a MCU+LEA platform with Unified NVM architecture. The\n                    <jats:sc>AND-MAC<\/jats:sc>\n                    mode in\n                    <jats:sc>InterAxNN<\/jats:sc>\n                    results in 91\u00d7\u2013127\u00d7 amount of additional forward progress over\n                    <jats:sc>XNOR-MAC<\/jats:sc>\n                    for 1%\u20132%, 4%\u201319%, and 1%\u20135% higher quality degradation for MNIST, EMNIST, and QMNIST, respectively.\n                  <\/jats:p>","DOI":"10.1145\/3771845","type":"journal-article","created":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T11:36:46Z","timestamp":1760355406000},"page":"1-44","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["InterAxNN: Reconfigurable and Approximate in-Memory Processing Accelerator for Ultra-Low-Power Binary Neural Network Inference in Intermittently Powered Systems"],"prefix":"10.1145","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8848-1069","authenticated-orcid":false,"given":"Arnab","family":"Raha","sequence":"first","affiliation":[{"name":"Intel Corporation","place":["Santa Clara, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9448-324X","authenticated-orcid":false,"given":"Sandeep Krishna","family":"Thirumala","sequence":"additional","affiliation":[{"name":"Micron Technology Inc","place":["Boise, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5609-9722","authenticated-orcid":false,"given":"Sumeet Kumar","family":"Gupta","sequence":"additional","affiliation":[{"name":"Purdue University","place":["West Lafayette, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4713-5386","authenticated-orcid":false,"given":"Vijay","family":"Raghunathan","sequence":"additional","affiliation":[{"name":"Purdue University","place":["West Lafayette, United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1109\/ISQED54688.2022.9806145","volume-title":"Proceedings of the 2022 23rd International Symposium on Quality Electronic Design (ISQED)","author":"Afzali-Kusha Hassan","year":"2022","unstructured":"Hassan Afzali-Kusha and Massoud Pedram. 2022. X-NVDLA: Runtime accuracy configurable NVDLA based on employing voltage overscaling approach. In Proceedings of the 2022 23rd International Symposium on Quality Electronic Design (ISQED). 7\u201312. DOI:10.1109\/ISQED54688.2022.9806145"},{"key":"e_1_3_2_3_2","first-page":"1","volume-title":"Proceedings of the 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS)","author":"Agbeyangi Abayomi O.","year":"2020","unstructured":"Abayomi O. Agbeyangi, Olaitan A. Alashiri, and Adeolu E. Otunuga. 2020. Automatic identification of vehicle plate number using raspberry pi. In Proceedings of the 2020 International Conference in Mathematics, Computer Engineering and Computer Science (ICMCECS). 1\u20134. DOI:10.1109\/ICMCECS47690.2020.246983"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.esr.2023.101124"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","unstructured":"M. Ali I. Chakraborty U. Saxena A. Agrawal A. Ankit and K. Roy. 2021. A 35.5-127.2 TOPS\/W dynamic sparsity-aware reconfigurable-precision compute-in-memory SRAM macro for machine learning. In IEEE Solid-State Circuits Letters 4 (2021) 129\u2013132. DOI:10.1109\/LSSC.2021.3093354","DOI":"10.1109\/LSSC.2021.3093354"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2023.3316121"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","unstructured":"Ahmedullah Aziz Evelyn T. Breyer An Chen Xiaoming Chen Suman Datta Sumeet Kumar Gupta Michael Hoffmann Xiaobo Sharon Hu Adrian Ionescu Matthew Jerry Thomas Mikolajick Halid Mulaosmanovic Kai Ni Michael Niemier Ian O\u2019Connor Atanu Saha Stefan Slesazeck Sandeep Krishna Thirumala and Xunzhao Yin. 2018. Computing with ferroelectric FETs: Devices models systems and applications. In 2018 Design Automation & Test in Europe Conference & Exhibition (DATE). 1289\u20131298. DOI:10.23919\/DATE.2018.8342213","DOI":"10.23919\/DATE.2018.8342213"},{"key":"e_1_3_2_8_2","first-page":"432","volume-title":"Proceedings of the 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers","author":"Bartling Steven C.","year":"2013","unstructured":"Steven C. Bartling, Sudhanshu Khanna, Michael P. Clinton, Scott R. Summerfelt, John A. Rodriguez, and Hugh P. McAdams. 2013. An 8MHz 75\u00b5A\/MHz zero-leakage non-volatile logic-based cortex-M0 MCU SoC exhibiting 100% digital state retention at VDD=0V with <400ns wakeup and sleep transitions. In Proceedings of the 2013 IEEE International Solid-State Circuits Conference Digest of Technical Papers. 432\u2013433. DOI:10.1109\/ISSCC.2013.6487802"},{"key":"e_1_3_2_9_2","first-page":"C202\u2013C203","volume-title":"Proceedings of the 2013 Symposium on VLSI Circuits","author":"Baumann A.","year":"2013","unstructured":"A. Baumann, M. Jung, K. Huber, M. Arnold, C. Sichert, S. Schauer, and R. Brederlow. 2013. A MCU platform with embedded FRAM achieving 350nA current consumption in real-time clock mode with full state retention and 6.5\u00b5s system wakeup time. In Proceedings of the 2013 Symposium on VLSI Circuits. C202\u2013C203."},{"key":"e_1_3_2_10_2","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1109\/ESSDERC.2019.8901735","volume-title":"Proceedings of the ESSDERC 2019\u201449th European Solid-State Device Research Conference (ESSDERC)","author":"Breyer Evelyn T.","year":"2019","unstructured":"Evelyn T. Breyer, Halid Mulaosmanovic, Jens Trommer, Thomas Melde, Stefan D\u00fcnkel, Martin Trentzsch, Sven Beyer, Thomas Mikolajick, and Stefan Slesazeck. 2019. Ultra-dense co-integration of FeFETs and CMOS logic enabling very-fine grained logic-in-memory. In Proceedings of the ESSDERC 2019\u201449th European Solid-State Device Research Conference (ESSDERC). 118\u2013121. DOI:10.1109\/ESSDERC.2019.8901735"},{"key":"e_1_3_2_11_2","first-page":"1","volume-title":"Proceedings of the 2019 56th ACM\/IEEE Design Automation Conference (DAC)","author":"Chen Wei-Ming","year":"2019","unstructured":"Wei-Ming Chen, Pi-Cheng Hsiu, and Tei-Wei Kuo. 2019. Enabling failure-resilient intermittently-powered systems without runtime checkpointing. In Proceedings of the 2019 56th ACM\/IEEE Design Automation Conference (DAC). 1\u20136."},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/MDAT.2019.2944094"},{"key":"e_1_3_2_13_2","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.23919\/DATE.2018.8342199","volume-title":"Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition (DATE)","author":"Chen Xiaoming","year":"2018","unstructured":"Xiaoming Chen, Xunzhao Yin, Michael Niemier, and Xiaobo Sharon Hu. 2018. Design and optimization of FeFET-based crossbars for binary convolution neural networks. In Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition (DATE). IEEE, 1205\u20131210. DOI:10.23919\/DATE.2018.8342199"},{"key":"e_1_3_2_14_2","doi-asserted-by":"crossref","first-page":"2921","DOI":"10.1109\/IJCNN.2017.7966217","volume-title":"Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN)","author":"Cohen G.","year":"2017","unstructured":"G. Cohen, S. Afshar, J. Tapson, and A. van Schaik. 2017. EMNIST: Extending MNIST to handwritten letters. In Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN). 2921\u20132926. DOI:10.1109\/IJCNN.2017.7966217"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","unstructured":"Jasper de Winkel Tom Hoefnagel Boris Blokland and Przemys\u0142aw Pawe\u0142czak. 2023. DIPS: Debug intermittently-powered systems like any embedded system. In Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems (SenSys \u201922) Association for Computing Machinery Boston Massachusetts 222\u2013235. DOI:10.1145\/3560905.3568543","DOI":"10.1145\/3560905.3568543"},{"key":"e_1_3_2_16_2","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/ISCAS.2016.7527198","volume-title":"Proceedings of the 2016 IEEE International Symposium on Circuits and Systems (ISCAS)","author":"Ding Caiwen","year":"2016","unstructured":"Caiwen Ding, Soroush Heidari, Yanzhi Wang, Yongpan Liu, and Jingtong Hu. 2016. Multi-source in-door energy harvesting for non-volatile processors. In Proceedings of the 2016 IEEE International Symposium on Circuits and Systems (ISCAS). 173\u2013176. DOI:10.1109\/ISCAS.2016.7527198"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","unstructured":"Zhouhang Jiang Zijian Zhao Shan Deng Yi Xiao Yixin Xu Halid Mulaosmanovic Stefan Duenkel Sven Beyer Scott Meninger Mohamed Mohamed Rajiv Joshi Xiao Gong Santosh Kurinec Vijaykrishnan Narayanan and Kai Ni. 2022. On the Feasibility of 1T Ferroelectric FET memory array. IEEE Transactions on Electron Devices 69 12 (2022) 6722\u20136730. DOI:10.1109\/TED.2022.3216819","DOI":"10.1109\/TED.2022.3216819"},{"key":"e_1_3_2_18_2","first-page":"1","volume-title":"Proceedings of the 2021 15th International Conference on Telecommunication Systems, Services, and Applications (TSSA)","author":"Firasanti Annisa","year":"2021","unstructured":"Annisa Firasanti, Tiara Eka Ramadhani, Muhammad Amin Bakri, and Eki Ahmad Zaki Hamidi. 2021. License plate detection using OCR method with raspberry pi. In Proceedings of the 2021 15th International Conference on Telecommunication Systems, Services, and Applications (TSSA). 1\u20135. DOI:10.1109\/TSSA52866.2021.9768252"},{"key":"e_1_3_2_19_2","first-page":"1","volume-title":"Proceedings of the 2023 China Semiconductor Technology International Conference (CSTIC)","author":"Fu Boyi","year":"2023","unstructured":"Boyi Fu, Jin Luo, Weikai Xu, Qianqian Huang, and Ru Huang. 2023. Design of ferroelectric FET-based capacitive-coupling computing-in-memory for binary neural networks. In Proceedings of the 2023 China Semiconductor Technology International Conference (CSTIC). 1\u20134. DOI:10.1109\/CSTIC58779.2023.10219283"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3589766"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","unstructured":"Nikolay Gospodinov and Georgi Krastev. 2024. Cyberphysical system for traffic sign detection and recognition. Engineering Proceedings 60 1 (2024). DOI:10.3390\/engproc2024060021","DOI":"10.3390\/engproc2024060021"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1021\/acsami.3c13945"},{"key":"e_1_3_2_23_2","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)","author":"Jacob Benoit","year":"2018","unstructured":"Benoit Jacob, Skirmantas Kligys, Bo Chen, Menglong Zhu, Matthew Tang, Andrew Howard, Hartwig Adam, and Dmitry Kalenichenko. 2018. Quantization and training of neural networks for efficient integer-arithmetic-only inference. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)."},{"key":"e_1_3_2_24_2","volume-title":"Proceedings of the 10th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP\u201916)","author":"Jain Vishal","year":"2016","unstructured":"Vishal Jain, Zitha Sasindran, Anoop Rajagopal, Soma Biswas, Harish S Bharadwaj, and K R Ramakrishnan. 2016. Deep automatic license plate recognition system. In Proceedings of the 10th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP\u201916). Association for Computing Machinery, New York, NY, USA, Article 6, 8 pages. DOI:10.1145\/3009977.3010052"},{"key":"e_1_3_2_25_2","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1145\/2627369.2631644","volume-title":"Proceedings of the 2014 IEEE\/ACM International Symposium on Low Power Electronics and Design (ISLPED)","author":"Jayakumar H.","year":"2014","unstructured":"H. Jayakumar, K. Lee, W. S. Lee, A. Raha, Y. Kim, and V. Raghunathan. 2014. Powering the internet of things. In Proceedings of the 2014 IEEE\/ACM International Symposium on Low Power Electronics and Design (ISLPED). 375\u2013380. DOI:10.1145\/2627369.2631644"},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.1145\/2700249"},{"key":"e_1_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/2983628"},{"key":"e_1_3_2_28_2","first-page":"6.2.1\u20136.2.4","volume-title":"Proceedings of the 2017 IEEE International Electron Devices Meeting (IEDM)","author":"Jerry Matthew","year":"2017","unstructured":"Matthew Jerry, Pai-Yu Chen, Jianchi Zhang, Pankaj Sharma, Kai Ni, Shimeng Yu, and Suman Datta. 2017. Ferroelectric FET analog synapse for acceleration of deep neural network training. In Proceedings of the 2017 IEEE International Electron Devices Meeting (IEDM). 6.2.1\u20136.2.4. DOI:10.1109\/IEDM.2017.8268338"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.35848\/1347-4065\/ac428a"},{"key":"e_1_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2020.3012217"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.1145\/3506732"},{"key":"e_1_3_2_32_2","first-page":"1322","volume-title":"Proceedings of the 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)","author":"Karthikayan P. N.","year":"2021","unstructured":"P. N. Karthikayan and R. Pushpakumar. 2021. Smart glasses for visually impaired using image processing techniques. In Proceedings of the 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). 1322\u20131327. DOI:10.1109\/I-SMAC52330.2021.9640715"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","unstructured":"Hyeonuk Kim Jaehyeong Sim Yeongjae Choi and Lee-Sup Kim. 2019. NAND-Net: Minimizing computational complexity of in-memory processing for binary neural Networks. In 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA). 661\u2013673. DOI:10.1109\/HPCA.2019.00017","DOI":"10.1109\/HPCA.2019.00017"},{"key":"e_1_3_2_34_2","volume-title":"Learning Multiple Layers of Features from Tiny Images","author":"Krizhevsky Alex","year":"2009","unstructured":"Alex Krizhevsky. 2009. Learning Multiple Layers of Features from Tiny Images. Technical Report. University of Toronto."},{"key":"e_1_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1109\/5.726791"},{"key":"e_1_3_2_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSII.2022.3233396"},{"key":"e_1_3_2_37_2","doi-asserted-by":"publisher","DOI":"10.1145\/3546193"},{"key":"e_1_3_2_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/JXCDC.2019.2923745"},{"key":"e_1_3_2_39_2","doi-asserted-by":"publisher","unstructured":"S. Lu and A Sengupta. 2020. Exploring the connection between binary and spiking neural networks. Front. Neurosci. 14 (2020) 535. DOI:10.3389\/fnins.2020.00535","DOI":"10.3389\/fnins.2020.00535"},{"key":"e_1_3_2_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3077575"},{"key":"e_1_3_2_41_2","first-page":"670","volume-title":"Proceedings of the 2015 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD)","author":"Ma Kaisheng","year":"2015","unstructured":"Kaisheng Ma, Xueqing Li, Yongpan Liu, John Sampson, Yuan Xie, and Vijaykrishnan Narayanan. 2015. Dynamic machine learning based matching of nonvolatile processor microarchitecture to harvested energy profile. In Proceedings of the 2015 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD). 670\u2013675. DOI:10.1109\/ICCAD.2015.7372634"},{"key":"e_1_3_2_42_2","unstructured":"Matrix PowerWatch. Retrieved Jan 2024 from https:\/\/www.matrixindustries.com\/prometheus"},{"key":"e_1_3_2_43_2","unstructured":"Bradley McDanel Surat Teerapittayanon and H.T. Kung. 2017. Embedded binarized neural networks. In Proceedings of the 2017 International Conference on Embedded Wireless Systems and Networks (EWSN \u201917). International Conference on Embedded Wireless Systems Uppsala Sweden 168\u2013173."},{"key":"e_1_3_2_44_2","doi-asserted-by":"publisher","DOI":"10.1145\/3476995"},{"key":"e_1_3_2_45_2","unstructured":"Colby Banbury Vijay Janapa Reddi Peter Torelli Jeremy Holleman Nat Jeffries Csaba Kiraly Pietro Montino David Kanter Sebastian Ahmed Danilo Pau Urmish Thakker Antonio Torrini Peter Warden Jay Cordaro Giuseppe Di Guglielmo Javier Duarte Stephen Gibellini Videet Parekh Honson Tran Nhan Tran Niu Wenxu and Xu Xuesong. 2021. MLPerf tiny benchmark. Retrieved from https:\/\/arxiv.org\/abs\/2106.07597"},{"key":"e_1_3_2_46_2","first-page":"1","volume-title":"Proceedings of the 2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)","author":"Mun HanGyeol","year":"2023","unstructured":"HanGyeol Mun, Hyunwoo Son, Seunghyun Moon, Jaehyun Park, ByungJun Kim, and Jae-Yoon Sim. 2023. A 28 nm 66.8 TOPS\/W sparsity-aware dynamic-precision deep-learning processor. In Proceedings of the 2023 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits). 1\u20132. DOI:10.23919\/VLSITechnologyandCir57934.2023.10185264"},{"key":"e_1_3_2_47_2","volume-title":"Proceedings of the NIPS Workshop on Deep Learning and Unsupervised Feature Learning","author":"Netzer Yuval","year":"2011","unstructured":"Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng. 2011. Reading digits in natural images with unsupervised feature learning. In Proceedings of the NIPS Workshop on Deep Learning and Unsupervised Feature Learning."},{"key":"e_1_3_2_48_2","unstructured":"NVIDIA Corporation. 2021. NVIDIA DeepStream SDK: Scalable AI Application Development. Retrieved from https:\/\/developer.nvidia.com\/deepstream-sdk. Accessed: 2025-08-02."},{"key":"e_1_3_2_49_2","unstructured":"OpenCores. [n.d.]. openMSP430. Retrieved May 2021 from https:\/\/opencores.org\/projects\/openmsp430"},{"key":"e_1_3_2_50_2","doi-asserted-by":"publisher","DOI":"10.1109\/JETCAS.2022.3169759"},{"key":"e_1_3_2_51_2","first-page":"315","volume-title":"Proceedings of the 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)","author":"Qiu Keni","year":"2020","unstructured":"Keni Qiu, Nicholas Jao, Mengying Zhao, Cyan Subhra Mishra, Gulsum Gudukbay, Sethu Jose, Jack Sampson, Mahmut Taylan Kandemir, and Vijaykrishnan Narayanan. 2020. ResiRCA: A resilient energy harvesting ReRAM crossbar-based accelerator for intelligent embedded processors. In Proceedings of the 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA). 315\u2013327. DOI:10.1109\/HPCA47549.2020.00034"},{"key":"e_1_3_2_52_2","first-page":"369-374","volume-title":"Proceedings of the 2020 on Great Lakes Symposium on VLSI (GLSVLSI\u201920)","author":"Qiu Keni","year":"2020","unstructured":"Keni Qiu, Mengying Zhao, Zhenge Jia, Jingtong Hu, Chun Jason Xue, Kaisheng Ma, Xueqing Li, Yongpan Liu, and Vijaykrishnan Narayanan. 2020. Design insights of non-volatile processors and accelerators in energy harvesting systems. In Proceedings of the 2020 on Great Lakes Symposium on VLSI (GLSVLSI\u201920). Association for Computing Machinery, New York, NY, USA, 369-374. DOI:10.1145\/3386263.3407596"},{"key":"e_1_3_2_53_2","first-page":"13","volume-title":"Proceedings of the 2021 IEEE 39th International Conference on Computer Design (ICCD)","author":"Raha Arnab","year":"2021","unstructured":"Arnab Raha, Soumendu Ghosh, Debabrata Mohapatra, Deepak A. Mathaikutty, Raymond Sung, Cormac Brick, and Vijay Raghunathan. 2021. Special session: Approximate TinyML systems: Full system approximations for extreme energy-efficiency in intelligent edge devices. In Proceedings of the 2021 IEEE 39th International Conference on Computer Design (ICCD). 13\u201316. DOI:10.1109\/ICCD53106.2021.00015"},{"key":"e_1_3_2_54_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2017.2767033"},{"key":"e_1_3_2_55_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2018.2864269"},{"key":"e_1_3_2_56_2","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1007\/978-3-319-46493-0_32","volume-title":"Proceedings of the Computer Vision\u2014ECCV 2016","author":"Rastegari Mohammad","year":"2016","unstructured":"Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, and Ali Farhadi. 2016. XNOR-Net: ImageNet classification using binary convolutional neural networks. In Proceedings of the Computer Vision\u2014ECCV 2016. Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.), Springer International Publishing, Cham, 525\u2013542."},{"key":"e_1_3_2_57_2","doi-asserted-by":"publisher","DOI":"10.1109\/JXCDC.2019.2930284"},{"key":"e_1_3_2_58_2","unstructured":"F. F. Rodrigues G. Riley and M. Luj\u00e1n. 2020. Energy predictive models for convolutional neural networks on mobile platforms. arXiv:2004.05137. Retrieved from https:\/\/arxiv.org\/abs\/2004.05137"},{"key":"e_1_3_2_59_2","doi-asserted-by":"publisher","unstructured":"Jos\u00e9 Mar\u00eda Rodr\u00edguez Corral Javier Civit-Masot Francisco Luna-Perej\u00f3n Ignacio D\u00edaz-Cano Arturo Morgado-Est\u00e9vez and Manuel Dom\u00ednguez-Morales. 2024. Energy efficiency in edge TPU vs. embedded GPU for computer-aided medical imaging segmentation and classification. Engineering Applications of Artificial Intelligence 127 (2024) 107298. DOI:10.1016\/j.engappai.2023.107298","DOI":"10.1016\/j.engappai.2023.107298"},{"key":"e_1_3_2_60_2","first-page":"184","volume-title":"Proceedings of the 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC)","author":"Sakimura Noboru","year":"2014","unstructured":"Noboru Sakimura, Yukihide Tsuji, Ryusuke Nebashi, Hiroaki Honjo, Ayuka Morioka, Kunihiko Ishihara, Keizo Kinoshita, Shunsuke Fukami, Sadahiko Miura, Naoki Kasai, Tetsuo Endoh, Hideo Ohno, Takahiro Hanyu, and Tadahiko Sugibayashi. 2014. 10.5 A 90nm 20MHz fully nonvolatile microcontroller for standby-power-critical applications. In Proceedings of the 2014 IEEE International Solid-State Circuits Conference Digest of Technical Papers (ISSCC). 184\u2013185. DOI:10.1109\/ISSCC.2014.6757392"},{"key":"e_1_3_2_61_2","first-page":"19.4.1\u201319.4.4","volume-title":"Proceedings of the 2021 IEEE International Electron Devices Meeting (IEDM)","author":"Salahuddin Saveef","year":"2021","unstructured":"Saveef Salahuddin, Ava Tan, Suraj Cheema, Nirmaan Shanker, Michael Hoffmann, and J.-H Bae. 2021. FeFETs for near-memory and in-memory compute. In Proceedings of the 2021 IEEE International Electron Devices Meeting (IEDM). 19.4.1\u201319.4.4. DOI:10.1109\/IEDM19574.2021.9720622"},{"key":"e_1_3_2_62_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41565-020-0655-z"},{"key":"e_1_3_2_63_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2024.3422012"},{"key":"e_1_3_2_64_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-42110-y"},{"key":"e_1_3_2_65_2","first-page":"109","volume-title":"Proceedings of the 2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP)","author":"Soliman Taha","year":"2020","unstructured":"Taha Soliman, Ricardo Olivo, Tobias Kirchner, Cecilia De la Parra, Maximilian Lederer, Thomas K\u00e4mpfe, Andre Guntoro, and Norbert Wehn. 2020. Efficient FeFET crossbar accelerator for binary neural networks. In Proceedings of the 2020 IEEE 31st International Conference on Application-specific Systems, Architectures and Processors (ASAP). 109\u2013112. DOI:10.1109\/ASAP49362.2020.00027"},{"key":"e_1_3_2_66_2","first-page":"1453","volume-title":"Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN)","author":"Stallkamp Johannes","year":"2011","unstructured":"Johannes Stallkamp, Marc Schlipsing, Jan Salmen, and Christian Igel. 2011. The german traffic sign recognition benchmark: A multi-class classification competition. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN). IEEE, 1453\u20131460."},{"key":"e_1_3_2_67_2","first-page":"T260\u2013T261","volume-title":"Proceedings of the 2017 Symposium on VLSI Technology","author":"Su Fang","year":"2017","unstructured":"Fang Su, Wei-Hao Chen, Lixue Xia, Chieh-Pu Lo, Tianqi Tang, Zhibo Wang, Kuo-Hsiang Hsu, Ming Cheng, Jun-Yi Li, Yuan Xie, Yu Wang, Meng-Fan Chang, Huazhong Yang, and Yongpan Liu. 2017. A 462GOPs\/J RRAM-based nonvolatile intelligent processor for energy harvesting IoE system featuring nonvolatile logics and processing-in-memory. In Proceedings of the 2017 Symposium on VLSI Technology. T260\u2013T261. DOI:10.23919\/VLSIT.2017.7998149"},{"key":"e_1_3_2_68_2","first-page":"1423","volume-title":"Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition (DATE)","author":"Sun Xiaoyu","year":"2018","unstructured":"Xiaoyu Sun, Shihui Yin, Xiaochen Peng, Rui Liu, Jae-sun Seo, and Shimeng Yu. 2018. XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks. In Proceedings of the 2018 Design, Automation and Test in Europe Conference and Exhibition (DATE). 1423\u20131428. DOI:10.23919\/DATE.2018.8342235"},{"key":"e_1_3_2_69_2","first-page":"782","volume-title":"Proceedings of the 22nd Asia and South Pacific Design Automation Conference (ASP-DAC)","author":"Tang Tianqi","year":"2017","unstructured":"Tianqi Tang, Lixue Xia, Boxun Li, Yu Wang, and Huazhong Yang. 2017. Binary convolutional neural network on RRAM. In Proceedings of the 22nd Asia and South Pacific Design Automation Conference (ASP-DAC). IEEE, 782\u2013787. DOI:10.1109\/ASPDAC.2017.7858419"},{"key":"e_1_3_2_70_2","unstructured":"Texas Instruments. [n.d.]. MSP430FR573x Mixed-Signal Microcontrollers. Retrieved May 2021 from www.ti.com\/lit\/gpn\/msp430fr5739"},{"key":"e_1_3_2_71_2","doi-asserted-by":"publisher","DOI":"10.1109\/TVLSI.2021.3125248"},{"key":"e_1_3_2_72_2","volume-title":"Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED\u201918)","author":"Thirumala S. K.","year":"2018","unstructured":"S. K. Thirumala, A. Raha, H. Jayakumar, K. Ma, V. Narayanan, V. Raghunathan, and S. K. Gupta. 2018. Dual mode ferroelectric transistor based non-volatile flip-flops for intermittently-powered systems. In Proceedings of the International Symposium on Low Power Electronics and Design (ISLPED\u201918). Association for Computing Machinery, New York, NY, USA, Article 31, 6 pages. DOI:10.1145\/3218603.3218653"},{"key":"e_1_3_2_73_2","first-page":"368","volume-title":"Proceedings of the 2020 IEEE 38th International Conference on Computer Design (ICCD)","author":"Thirumala Sandeep Krishna","year":"2020","unstructured":"Sandeep Krishna Thirumala, Arnab Raha, Vijay Raghunathan, and Sumeet Kumar Gupta. 2020. IPS-CiM: Enhancing energy efficiency of intermittently-powered systems with compute-in-memory. In Proceedings of the 2020 IEEE 38th International Conference on Computer Design (ICCD). 368\u2013376. DOI:10.1109\/ICCD50377.2020.00068"},{"key":"e_1_3_2_74_2","doi-asserted-by":"publisher","DOI":"10.3389\/felec.2022.833260"},{"key":"e_1_3_2_75_2","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.3024167"},{"key":"e_1_3_2_76_2","volume-title":"Cold Case: The Lost MNIST Digits","author":"Yadav C.","year":"2019","unstructured":"C. Yadav and L. Bottou. 2019. Cold Case: The Lost MNIST Digits. Curran Associates Inc., Red Hook, NY, USA."},{"key":"e_1_3_2_77_2","doi-asserted-by":"publisher","DOI":"10.1109\/TC.2021.3104736"},{"key":"e_1_3_2_78_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCAD.2022.3197513"},{"key":"e_1_3_2_79_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSSC.2019.2963616"},{"key":"e_1_3_2_80_2","first-page":"1","volume-title":"Proceedings of the 2018 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD)","author":"Zhu Zhenhua","year":"2018","unstructured":"Zhenhua Zhu, Jilan Lin, Ming Cheng, Lixue Xia, Hanbo Sun, Xiaoming Chen, Yu Wang, and Huazhong Yang. 2018. Mixed size crossbar based RRAM CNN accelerator with overlapped mapping method. In Proceedings of the 2018 IEEE\/ACM International Conference on Computer-Aided Design (ICCAD). 1\u20138. DOI:10.1145\/3240765.3240825"},{"key":"e_1_3_2_81_2","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1109\/ISSCC.2011.5746342","volume-title":"Proceedings of the 2011 IEEE International Solid-State Circuits Conference","author":"Zwerg Michael","year":"2011","unstructured":"Michael Zwerg, Adolf Baumann, R\u00fcdiger Kuhn, Matthias Arnold, Ronald Nerlich, Marcus Herzog, Ralph Ledwa, Christian Sichert, Volker Rzehak, Priya Thanigai, and Bjoern Oliver Eversmann. 2011. An 82uA\/MHz microcontroller with embedded FeRAM for energy-harvesting applications. In Proceedings of the 2011 IEEE International Solid-State Circuits Conference. 334\u2013336. DOI:10.1109\/ISSCC.2011.5746342"}],"container-title":["ACM Transactions on Design Automation of Electronic Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3771845","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:56:31Z","timestamp":1762610191000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3771845"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,8]]},"references-count":80,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,31]]}},"alternative-id":["10.1145\/3771845"],"URL":"https:\/\/doi.org\/10.1145\/3771845","relation":{},"ISSN":["1084-4309","1557-7309"],"issn-type":[{"type":"print","value":"1084-4309"},{"type":"electronic","value":"1557-7309"}],"subject":[],"published":{"date-parts":[[2025,11,8]]},"assertion":[{"value":"2024-08-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-07","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-08","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}