{"id":14,"date":"2017-07-07T20:21:07","date_gmt":"2017-07-07T20:21:07","guid":{"rendered":"http:\/\/utsaengineer.wpengine.com\/faculty-page-example\/?page_id=14"},"modified":"2026-04-16T19:19:44","modified_gmt":"2026-04-17T00:19:44","slug":"publications","status":"publish","type":"page","link":"https:\/\/ceid.utsa.edu\/ejohn\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

Granted<\/u> Patents:<\/u><\/span><\/h4>\n
    \n
  • US Patent # 12,261,937<\/strong>, Mar 2025: “Method & Apparatus for an Ultra Low Power VLSI Implementation of the 128-Bit AES Algorithm Using a Novel Approach to the Shiftrow Transformation”, (Additional claims on Patent # 11,948,596) A. Muthineni and E. John<\/span><\/li>\n<\/ul>\n
      \n
    • US Patent # 12,094,243, <\/strong>Sep 2024, <\/span>“Method and Apparatus for Discreet Facial Recognition on Pocket-Size Offline Mobile Platform with Augmented Reality Feedback with Real-Time Training Capability for Usage by Universal Users”, P. Stockton and E. John.<\/li>\n<\/ul>\n
        \n
      • US Patent # 11,948,596, <\/strong>April 2024, “<\/span>Method and Apparatus for Defending Against Laser or Other Electromagnetic Wave-Based Audio Injection Attacks on Voice-Controllable Devices and Systems”. E. John and R. Krishnan.<\/li>\n<\/ul>\n
          \n
        • US Patent # 11,838,403<\/strong>, Dec 2023: “Method & Apparatus for an Ultra Low Power VLSI Implementation of the 128-Bit AES Algorithm Using a Novel Approach to the Shiftrow Transformation”,\u00a0 A. Muthineni and E. John.<\/span><\/li>\n<\/ul>\n
            \n
          • US Patent # 10,891,110, <\/strong>Jan, 2021: <\/span>“AES\/CRC Engine Based on Resource Shared Galois Field Computation”\u00a0S. Mostafa and E. John.<\/span><\/li>\n<\/ul>\n
              \n
            • US Patent # 10,359,832, <\/strong>July, 2019: “Method and Apparatus for Reducing Power and Cycle Requirement for FFT of ECG signals”, E. John and S. Mostafa<\/span><\/li>\n<\/ul>\n
                \n
              • US Patent # 8,250,350<\/strong>, August 21, 2012: \u201cComputer systems with non-volatile write-protected memory based operating systems and secure architecture\u201d, E.\u00a0John, Thomas John and Lizy John.<\/span><\/li>\n<\/ul>\n
                  \n
                • US Patent # 8,214,629<\/strong>, July 3, 2012: \u201cComputer systems with secure instantly available applications using non-volatile write protected memory\u201d, E.\u00a0 John, Thomas John and Lizy John.<\/span><\/li>\n<\/ul>\n
                    \n
                  • US Patent #<\/strong> 6,824,480, <\/strong>November 30, 2004: \u201cMethod and apparatus for location of objects, and application to real time display of the position of players, equipment and officials during a sporting event\u201d, E.\u00a0John and H. Foltz.<\/span><\/li>\n<\/ul>\n

                    Pending Patent Applications<\/u><\/strong><\/span><\/h2>\n
                      \n
                    • \u201cSystem and Method for Time Series Pattern Matching for Energy Efficient Applications\u201d, P. Sengupta and E. John, Application submitted to USPTO on August 2024.<\/li>\n<\/ul>\n
                        \n
                      • “Semi-Streaming Data Flow Architecture with Layer-Type Specialized Processing Engines for the Implementation of Convolutional Neural Networks in Hardware.”, N. Shayduk and E. John; Application filed on April, 2022<\/li>\n<\/ul>\n

                        Book(s):<\/strong><\/span><\/h2>\n
                          \n
                        • “Fundamentals of Logic Design”, Enhanced <\/span>7th Edition, <\/span>by Charles H. Roth, Jr.,\u00a0 Larry L. Kinney, and Eugene B. John<\/strong>, CENGAGE Learning, Boston, MA.\u00a0 (1\/1\/2020).<\/span><\/li>\n<\/ul>\n
                            \n
                          • E. John<\/strong> and J. Rubio, \u201cUnique Chips and Systems\u201d edited book; CRC Press, November 2007\n
                            \n

                            \n<\/div>\n<\/li>\n<\/ul>\n

                            Book Chapters: <\/strong><\/span><\/h2>\n
                              \n
                            • C. Davis, P. Stockton, Z. Susskind, E. John<\/strong> and L. K. John, “Characterization of Neuro-Symbolic AI and Graph Convolutional Network Workloads”, in Artificial Intelligence: Machine Learning, Convolutional Neural Networks and Large Language Models<\/em>, Editors: L. Deligiannidis, G. Dimitoglou, H. R. Arabnia, and A. P. Tafti, Publisher: De Gruyter, Berlin, Germany; July, 2024.<\/span><\/li>\n<\/ul>\n
                                \n
                              • E. John<\/strong>, \u201cSemiconductor Memory Circuits\u201d, in the Computer Engineering Hand Book<\/em>, 2nd<\/sup> ed. Editor: V. Oklobdzija, CRC Press, pp. 5-1 to 5-27, 2008.<\/li>\n<\/ul>\n
                                  \n
                                • E. John<\/strong>, \u201cVLSI Circuits\u201d, in the Computer Engineering Hand Book<\/em>, Editor: V. Oklobdzija, CRC Press, pp. 2.1 \u2013 2.20, 2002<\/li>\n<\/ul>\n
                                    \n
                                  • L. John and E. John<\/strong>, \u201cBit-Slice Computers\u201d, in the Encyclopedia of Electrical and Electronics Engineering<\/em>, Supplement 1, Editor: J.G.Webster, John Wiley and Sons, Inc. pp. 39-44, 1999.<\/li>\n<\/ul>\n

                                    Journal and Conference Publications:<\/strong><\/span><\/h2>\n
                                      \n
                                    • C. Davis, P. Stockton, E. Shojae, J. Ryoo and E. John <\/strong>” Hardware Acceleration for Graph Neural Networks” accepted for publications in the <\/span>23rd ACM International Conference on Computing Frontiers (CF’26) May 19-21, 2026, Catania, Sicily, Italy (Poster Session).<\/li>\n<\/ul>\n
                                        \n
                                      • S. Aphale and E<\/strong>. John<\/strong>, “Integrating Vision Transformers and Generative Models for High Accuracy Breast Cancer Detection from Histopathology Images” in the 5th IEEE International Conference on Computing and Machine Intelligence (ICMI-2026), April 9 – 10, Al Ahsa, Saudi Arabia.<\/span><\/li>\n<\/ul>\n
                                          \n
                                        • K. Baby, L. K. John, P. Lima, F. Franca and E. John<\/strong>, “CA-WNN: Improving Weightless Neural Networks using Cellular Automata Feature Extraction”, in the 5th IEEE International Conference on Computing and Machine Intelligence (ICMI-2026), April 9 – 10, Al Ahsa, Saudi Arabia.<\/span><\/li>\n<\/ul>\n
                                            \n
                                          • A. Jha, S. Nag, I. D. S. Miranda, F. M. G. Franca, L. John, P. M. V. Lima and E. John<\/strong>, \u201cArrhythmia Classification at the Edge Using a Weightless Neural Network Hardware Accelerator\u201d, The 2<\/span>2nd<\/span> International Symposium on Applied Reconfigurable Computing (ARC-2026), <\/span>\u00a0<\/span>April 8-10, 2026, Cagliari, Italy.<\/span><\/span><\/li>\n<\/ul>\n
                                              \n
                                            • T. Kholay, A. Kedilaya, A. Arora, E. John<\/strong>, J. Kulkarni and L. John “\u201cLearning Based Presilicon Estimation of Design Area from Early EDA Metrics”, <\/span>The 22nd<\/span> International Symposium on Applied Reconfigurable Computing (ARC-2026), <\/span><\/span>\u00a0<\/span>April 8-10, 2026, Cagliari, Italy.<\/span><\/li>\n<\/ul>\n
                                                \n
                                              • E. John <\/strong>and L. John, “Artificial Intelligence and Smart Sensors Usher a New Era in Patient Care”, IEEE Computer Magazine, pp 85-94, January 2026.<\/span><\/li>\n<\/ul>\n
                                                  \n
                                                • I. D. S. Miranda, V. Pillai, T. Musale, M. Jadhao, P. C. R. S. Neto, Z. Susskind, A. Bacellar, M. Lhostis, P.\u00a0 M. V. Lima, D. L. C. Dutra, E. John<\/strong>, M. Breternitz Jr., F. M. G. Franc\u00b8a , E. Ozer, and L. K. John,\u00a0 “Weightless Neural Networks on Flexible Substrates: A Novel Approach to Wearable Machine Learning”,\u00a0 in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, pp 444 – 452,\u00a0<\/span>\n
                                                  Volume: 34, Issue 2, February 2026.<\/div>\n<\/li>\n<\/ul>\n
                                                    \n
                                                  • S. Aphale, A. Kalapa, E. Tolliver, and E. John<\/strong>, “Synthetic Medical Image Generation with Conditional Generative Adversarial Networks for Enhanced Diagnostic Performance”, in The\u00a0 12th Annual International Conference on Computational Science and Computational Intelligence (CSCI-25), December 3-5, 2025 – Las Vegas, USA.<\/span><\/li>\n<\/ul>\n
                                                      \n
                                                    • E. Shojae, W. Dai, A. Jha, E. John<\/strong> and J. Ryoo, “Hardware-Based Reinforcement Learning for Adaptive Dynamic Voltage and Frequency Scaling”, 8th IBM\/IEEE AI Compute Symposium, November 13,\u00a0 2025 (Poster Session).<\/li>\n<\/ul>\n
                                                        \n
                                                      • P. Stockton, C. Davis, J. Ryoo and E. John<\/strong>, “Accelerating Neuro-Symbolic AI with CUTLASS-Optimized CUDA Kernels”, 8th IBM\/IEEE AI Compute Symposium, November 13,\u00a0 2025 (Poster Session).<\/span><\/li>\n<\/ul>\n
                                                          \n
                                                        • S. Nag, A. Bacellar, Z. Susskind, A. Jha, L. Liberty, A. Sivakumar, E. John<\/strong>, K. Kailas, P. Lima, N. Yadwadkar, F. Fran\u00e7a, L. K. John, “LL-ViT: Edge Deployable Vision Transformers with Look Up Table Neurons”, the <\/span><\/span>International Conference on Field Programmable Technology (FPT 2025), <\/span>2-5th December 2025, Shanghai, China<\/li>\n<\/ul>\n
                                                            \n
                                                          • L. John, P. Lima, A. Bacellar, S. Nag, E. John<\/strong> and F. Fran\u00e7a “LUT Based Neural Networks as Neuro-Symbolic Systems”, <\/span><\/span>19th International Conference on Neurosymbolic Learning and Reasoning (NeSy 2025)<\/em>, Sep 2025, Santa Cruz, CA.<\/li>\n<\/ul>\n
                                                              \n
                                                            • G. Jha, A. Jha and E. John<\/strong>, “An EEG Based High Accuracy CNN for Emotional Health Detection”, the 27th International Conference on Artificial Intelligence (ICAI-25)<\/em>, July 21 – 24, 2025, Las Vegas, NV.<\/span><\/li>\n<\/ul>\n
                                                                \n
                                                              • J. Hollen and E. John<\/strong>, “Mars Surface Novelty Detection Using Binarized Neural Networks”, MWSCAS 2025, <\/em><\/span>IEEE International Midwest Symposium on Circuits and Systems<\/em>,\u00a0 August 10-13, 2025, Lansing, MI.<\/li>\n<\/ul>\n
                                                                  \n
                                                                • C. Davis, P. Stockton, J. Ryoo and E. John<\/strong>, “Improving Energy Efficiency of Graph Neural Network Execution by using PIM Architecture”,\u00a0 1st International Workshop on Data Center Energy Efficiency (DCEE-2025)<\/em> @ISCA 2025, June 21, 2025, Tokyo, Japan.<\/li>\n<\/ul>\n
                                                                    \n
                                                                  • S. Aphale and E. John<\/strong>, “Cross Framework Comparative Analysis of Deep-ArrhyNet for High-Accuracy Arrhythmia Classification”, the 17th International Conference on Smart Computing and Artificial Intelligence (SCAI 2025),<\/em> July 13-19, Kitakyushi, Japan.<\/span><\/li>\n<\/ul>\n
                                                                      \n
                                                                    • V. Pillai, S. Nag, M. Jadhao, A. Bacellar, I. Miranda, F. Franca, P. Lima, D. Dutra, L. John and E. John<\/strong>, “Edge-Optimized Weightless Neural Network for Low-Power Wearable Arrhythmia Detection”, IEEE 18th Dallas Circuits and Systems Conference (DCAS)<\/em>, Dallas, TX, April, 2025.<\/span><\/li>\n<\/ul>\n
                                                                        \n
                                                                      • C. Davis, P. Stockton, M. Zhang, J. Ryoo and E. John<\/strong>, “Microarchitectural Characterization of LightGCN and ExpressGNN and Architectural Implications”, (Poster Presentation) HiPEAC 2025: The 20th International Conference on High Performance, Edge and Cloud Computing<\/em>, Jan 20 – 22, 2025, Barcelona, Spain.<\/li>\n<\/ul>\n
                                                                          \n
                                                                        • G. Jha, A. Jha and E. John<\/strong>, ” Performance Comparison of High Accuracy CNNs for Brain Tumor Detection Using Transfer Learning”\u00a0\u00a0t<\/em>he 2024 International Conference on Computational Science and Computational Intelligence (CSCI’24)<\/em>: December 11-13, 2024; Las Vegas.<\/li>\n<\/ul>\n
                                                                            \n
                                                                          • V. Pillai, I. Miranda, T Musale, M. Jadhao, P. Neto, Z. Susskind, A. Bacellar, M. Lhostis, P. Lima, D. Dutra, E. John<\/strong>, M. Breternitz Jr., F. Franca, E. Ozer and L. K. John, \u201carrWNN: Arrhythmia-detecting Weightless Neural Network FlexIC\u201d, in the Proceeding of the IEEE <\/em>International Flexible Electronics Technology Conference (IFETC-2024)<\/em>, Bologna, Italy, <\/span>15-18 September 2024.<\/li>\n<\/ul>\n
                                                                              \n
                                                                            • S. Nag, Z. Susskind, A. Arora, A. Bacellar, D. Dutra, I Miranda, K. Kailas, E. John<\/strong>, M Breternitz Jr., P. Lima, F. Franca, L. K. John, “LogicNets vs. ULEEN : Comparing two novel high throughput edge ML inference techniques on FPGA”, in the Proceeding of the 67th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS 2024)<\/em>, Springfield, MA, August 11 \u2013 14,\u00a0 2024.<\/li>\n<\/ul>\n
                                                                                \n
                                                                              • G. Jha, A Jha and E. John<\/strong>,\u00a0 “A High Accuracy CNN for Breast Cancer Detection using Mammography Images”,\u00a067th <\/em>\u00a0IEEE International Midwest Symposium on Circuits and Systems (MWSCAS 2024), Springfield, MA<\/em>, August 11 – 14, <\/span>\u00a02024.<\/span><\/li>\n<\/ul>\n
                                                                                  \n
                                                                                • A. Bacellar, Z. Susskind, M. Breternitz Jr, E. John, <\/strong>L. K. John, P. Lima and F. Franca, “Differentiable Weightless Neural Networks”, in the proceedings of the\u00a0 41st International Conference on Machine Learning (ICML-2024)<\/em>, Vienna, Austria, July 21-27, 2024.<\/li>\n<\/ul>\n
                                                                                    \n
                                                                                  • P. Sengupta and E. John<\/strong> “Time Series Pattern Matching Technique for Low-Energy Arrhythmia Classification in Implantable Cardiac Devices” in the proceedings of the 3rd IEEE International Conference on Computing and Machine Intelligence (ICMI-2024)<\/em>. Michigan, April 13-14, 2024.<\/li>\n<\/ul>\n
                                                                                      \n
                                                                                    • A. Owahid, and E. John, <\/strong>“Instruction Profiling Based Predictive Throttling for Power and Performance”, in\u00a0IEEE Transactions on Computers<\/em>, vol. 72, no. 12, pp. 3532-3545, Dec. 2023.<\/li>\n<\/ul>\n
                                                                                        \n
                                                                                      • L. John, F. Franca, S. Mitra, Z. Susskind, P. Lima, I. Miranda, E. John<\/strong>, D. Dutra and M. Breternitz, “Dendrite-inspired Computing to improve Resilience of Neural Networks to Faults in Emerging Memory Technologies”, in the\u00a0Proceedings of <\/em>the 8th IEEE International Conference on Rebooting Computing (ICRC 2023),<\/em> 5-6, December, 2023; San Diego, CA.<\/li>\n<\/ul>\n
                                                                                          \n
                                                                                        • P. Stockton, C. Davis and E. John<\/strong>, “Microarchitecture Characterization and Analysis of Emerging Neuro-Symbolic A.I. Workloads”, in the\u00a0Proceedings of t<\/em>he 2023 International Conference on Computational Science and Computational Intelligence (CSCI’23)<\/em>: December 13-15, 2023; Las Vegas.<\/li>\n<\/ul>\n
                                                                                            \n
                                                                                          • C. Davis, P. Stockton and E. John<\/strong>, “Profiling Analysis for Enhancing the Performance of Graph Neural Networks”, in the <\/span>Proceedings of the 22nd IEEE International Conference on Machine Learning and Applications<\/em>, December 15-17, 2023, Jacksonville, Florida.<\/span><\/li>\n<\/ul>\n
                                                                                              \n
                                                                                            • P. Stockton, and E. John<\/strong>, \u201cNeuro-Symbolic Workload Micro-Architecture Analysis and Characterization of the Neural Logic Machine\u201d, SRC (Semiconductor Research Corporation) TECHCON 2023, Austin, Texas, September 10-12, 2023.<\/li>\n<\/ul>\n
                                                                                                \n
                                                                                              • Y. Oleyaeimotlagha, T. Banerjee, A. Taha and E. John<\/strong>, “Quickest Change Detection in Statistically Periodic Processes with Unknown Post-Change Distribution”, Sequential Analysis: Design Methods and Applications, Pages 404-437, Dec 2023.<\/em><\/li>\n<\/ul>\n
                                                                                                  \n
                                                                                                • C. Davis, P. Stockton, Z. Susskind, E. John<\/strong>, and Lizy K. John, “Characterization of Emerging AI Workloads: Neural Logic Machines and Graph Convolutional Networks” the 2022 International Conference on Computational Science and Computational Intelligence (CSCI’22),<\/em> December 14-16, 2022; Las Vegas.<\/li>\n<\/ul>\n
                                                                                                    \n
                                                                                                  • E. Tolliver, V. Pillai, A. Jha and E. John<\/strong>, “A Comparative Analysis of Half Precision Floating Point Representations in MACs for Deep Learning”, in the Proc. of the International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), <\/em>16-18 November 2022, Maldives<\/em><\/li>\n<\/ul>\n
                                                                                                      \n
                                                                                                    • S. Aphale, A. Jha, and E. John<\/strong>, “High Accuracy Arrhythmia Classification using Transfer Learning with Fine-Tuning”\u00a0 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON),<\/em> Virtual Conference, New York, USA 26 – 29 October, 2022<\/li>\n<\/ul>\n
                                                                                                        \n
                                                                                                      • A. Jha, E. John<\/strong>, and T. Banerjee, “Multi-Class Classification of Dementia from MRI Images Using Transfer Learning”\u00a0 2022 IEEE 13th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON),<\/em> Virtual Conference, New York, USA 26 – 29 October, 2022<\/span><\/li>\n<\/ul>\n
                                                                                                          \n
                                                                                                        • A. Jha, E. John<\/strong>\u00a0and T. Banerjee,” Transfer Learning for COVID-19 and Pneumonia Detection using Chest X-Rays”, 65th <\/em>\u00a0IEEE International Midwest Symposium on Circuits and Systems (MWSCAS 2022), <\/em>\u00a0 Fukuoka, Japan (Virtual Conference), August 7 – 10, <\/span>\u00a02022.<\/li>\n<\/ul>\n
                                                                                                            \n
                                                                                                          • C. Davis and E. John<\/strong>, “Shared Round Core Architecture: A Novel AES Implementation for Implantable Cardiac Devices”, 65th <\/em>\u00a0IEEE International Midwest Symposium on Circuits and Systems (MWSCAS 2022), <\/em>\u00a0 Fukuoka, Japan (Virtual Conference), August 7 – 10, <\/span>\u00a02022.<\/span><\/li>\n<\/ul>\n
                                                                                                              \n
                                                                                                            • W. Chen, T. Banerjee, E. John<\/strong>, “A Meta-Transfer Learning Approach to ECG Arrhythmia Detection”, the\u00a0 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, United Kingdom 11-15 July 2022.<\/li>\n<\/ul>\n
                                                                                                                \n
                                                                                                              • S. D. Sakyi,\u00a0 V. Kothavade and E.\u00a0 John<\/strong>, “Breast Cancer Detection using Deep Learning”, Viva Science SA, San Antonio, <\/span>April 9, 2022 (Poster Session).<\/span><\/li>\n<\/ul>\n
                                                                                                                  \n
                                                                                                                • Z. Susskind, B. Arden, L. K. John, P. Stockton, and E. B. John<\/strong>, “Characterizing Neuro-Symbolic Workloads”, IBM IEEE CAS\/EDS AI Compute Symposium, October 13-14,\u00a0 2021 (Poster Session).<\/li>\n<\/ul>\n
                                                                                                                    \n
                                                                                                                  • S. Aphale, E. John<\/strong> and T. Banerjee, “ArrhyNet: A High Accuracy Arrhythmia Classification Convolutional Neural Network”, the 2021 IEEE 64th International Midwest Symposium on Circuits & Systems (MWSCAS 2021), August 9-11, 2021<\/li>\n<\/ul>\n
                                                                                                                      \n
                                                                                                                    • T. <\/span>Banerjee<\/span>, A. Taha and E. John<\/strong>, “Robust Quickest Change Detection in Statistically Periodic Processes”, the 2021 IEEE International Symposium on Information Theory (ISIT-2021), Melbourne, Victoria, Australia, 12-20 July, 2021<\/li>\n<\/ul>\n
                                                                                                                        \n
                                                                                                                      • Banerjee, S. Padhy, A. Taha and E. John<\/strong>, \u201cQuickest Joint Detection and Classification of Faults in Statistically Periodic Processes, In Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Toronto, Canada, June 2021<\/li>\n<\/ul>\n
                                                                                                                          \n
                                                                                                                        • N. Shaydyuk and E. John<\/strong>, “FPGA Implementation of MobileNetV2 CNN Model Using Semi-Streaming Architecture for Low Power Inference\u00a0 Applications”, 18th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA-2020), England, UK, 17-19, pp. 160-167, December 2020.<\/li>\n<\/ul>\n
                                                                                                                            \n
                                                                                                                          • K. Leochico and E. John, <\/strong>“Evaluating Cloud Auto-Scaler Resource Allocation Planning Under High-Performance Computing Workloads”, 19th International Conference on Ubiquitous Computing and Communications (IUCC-2020), UK,\u00a0 December 2020.<\/li>\n<\/ul>\n
                                                                                                                              \n
                                                                                                                            • S. Verma, Q. Wu, B. Hanindhito, G. Jha, E. John<\/strong>, R. Radhakrishnan and L. K. John, \u201cDemystifying the MLPerf Training Benchmark Suite\u201d, In the Proc. of the 2020 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS’20), Boston, MA, August 2020.<\/li>\n<\/ul>\n
                                                                                                                                \n
                                                                                                                              • T. Banerjee, E. Adib, A. Taha and E. John<\/strong>, “Sequential methods for detecting a change in the distribution of an episodic process,” In the Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Virtual Barcelona, Spain, May 2020.<\/li>\n<\/ul>\n
                                                                                                                                  \n
                                                                                                                                • T. K. Kodali, Y. Zhang, E. John<\/strong> and W. Lin, \u201cAn Asynchronous High-Performance Approximate Adder with Low-Cost Error Correction\u201d, Journal of Information Science and Engineering, pp 1-12<\/em>, Jan 2020.<\/li>\n<\/ul>\n