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2020 – today
- 2024
[j28]Nicholas Soures
, Jayanta Dey, Dhireesha Kudithipudi:
Learning Continually in Silicon. Computer 57(10): 160-164 (2024)- [j27]Abdullah M. Zyarah, Alaa M. Abdul-Hadi, Dhireesha Kudithipudi:
Reservoir Network With Structural Plasticity for Human Activity Recognition. IEEE Trans. Emerg. Top. Comput. Intell. 8(5): 3228-3238 (2024) - [j26]Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. Trans. Mach. Learn. Res. 2024 (2024) - [c63]Nicholas Soures, Vedant Karia, Dhireesha Kudithipudi:
Advancing Neuro-Inspired Lifelong Learning for Edge with Co-Design. AAAI Spring Symposia 2024: 317 - [c62]Vedant Karia, Abdullah M. Zyarah, Dhireesha Kudithipudi:
PositCL: Compact Continual Learning with Posit Aware Quantization. ACM Great Lakes Symposium on VLSI 2024: 645-650 - [i27]Gido M. van de Ven, Nicholas Soures, Dhireesha Kudithipudi:
Continual Learning and Catastrophic Forgetting. CoRR abs/2403.05175 (2024) - [i26]Fatima Tuz Zohora, Vedant Karia, Nicholas Soures, Dhireesha Kudithipudi:
Probabilistic Metaplasticity for Continual Learning with Memristors. CoRR abs/2403.08718 (2024) - [i25]Truman Hickok, Dhireesha Kudithipudi:
Watch Your Step: Optimal Retrieval for Continual Learning at Scale. CoRR abs/2404.10758 (2024) - [i24]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System. CoRR abs/2405.13347 (2024) - [i23]Nicholas Soures, Peter Helfer, Anurag Reddy Daram, Tej Pandit, Dhireesha Kudithipudi:
TACOS: Task Agnostic Continual Learning in Spiking Neural Networks. CoRR abs/2409.00021 (2024) - 2023
- [j25]Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha:
A domain-agnostic approach for characterization of lifelong learning systems. Neural Networks 160: 274-296 (2023) - [c61]Peter Helfer, Corinne Teeter, Aaron J. Hill, Craig M. Vineyard, James B. Aimone, Dhireesha Kudithipudi:
Context Modulation Enables Multi-tasking and Resource Efficiency in Liquid State Machines. ICONS 2023: 17:1-17:9 - [c60]Anurag Reddy Daram, Dhireesha Kudithipudi:
NEO: Neuron State Dependent Mechanisms for Efficient Continual Learning. NICE 2023: 11-19 - [e1]Dhireesha Kudithipudi, Charlotte Frenkel, Suma Cardwell, James B. Aimone:
Neuro-Inspired Computational Elements Conference, NICE2023, San Antonio, TX, USA, April 11-14, 2023. ACM 2023, ISBN 978-1-4503-9947-0 [contents] - [i22]Megan M. Baker, Alexander New, Mario Aguilar-Simon, Ziad Al-Halah, Sébastien M. R. Arnold, Eseoghene Ben-Iwhiwhu, Andrew P. Brna, Ethan Brooks, Ryan C. Brown, Zachary Daniels, Anurag Reddy Daram, Fabien Delattre, Ryan Dellana, Eric Eaton, Haotian Fu, Kristen Grauman, Jesse Hostetler, Shariq Iqbal, Cassandra Kent, Nicholas Ketz, Soheil Kolouri, George Dimitri Konidaris, Dhireesha Kudithipudi, Erik G. Learned-Miller, Seungwon Lee, Michael Littman, Sandeep Madireddy, Jorge A. Mendez, Eric Q. Nguyen, Christine D. Piatko, Praveen K. Pilly, Aswin Raghavan, Abrar Rahman, Santhosh Kumar Ramakrishnan, Neale Ratzlaff, Andrea Soltoggio, Peter Stone, Indranil Sur, Zhipeng Tang, Saket Tiwari, Kyle Vedder, Felix Wang, Zifan Xu, Angel Yanguas-Gil, Harel Yedidsion, Shangqun Yu, Gautam K. Vallabha:
A Domain-Agnostic Approach for Characterization of Lifelong Learning Systems. CoRR abs/2301.07799 (2023) - [i21]Jason Yik, Soikat Hasan Ahmed, Zergham Ahmed, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Douwe den Blanken, Petrut Bogdan, Sander M. Bohté, Younes Bouhadjar, Sonia M. Buckley, Gert Cauwenberghs, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Reddy Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Jeremy Forest, Steve B. Furber, Michael Furlong, Aditya Gilra, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Gregor Lenz, Rajit Manohar, Christian Mayr, Konstantinos P. Michmizos, Dylan R. Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayça Özcelikkale, Noah Pacik-Nelson, Priyadarshini Panda, Pao-Sheng Sun, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Catherine D. Schuman, Jae-sun Seo, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Kenneth Michael Stewart, Terrence C. Stewart, Philipp Stratmann, Guangzhi Tang, Jonathan Timcheck, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Biyan Zhou, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi:
NeuroBench: Advancing Neuromorphic Computing through Collaborative, Fair and Representative Benchmarking. CoRR abs/2304.04640 (2023) - [i20]Dhireesha Kudithipudi, Anurag Reddy Daram, Abdullah M. Zyarah, Fatima Tuz Zohora, James B. Aimone, Angel Yanguas-Gil, Nicholas Soures, Emre Neftci, Matthew Mattina, Vincenzo Lomonaco, Clare D. Thiem, Benjamin R. Epstein:
Design Principles for Lifelong Learning AI Accelerators. CoRR abs/2310.04467 (2023) - [i19]Eli Verwimp, Rahaf Aljundi, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L. Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H. Lampert, Martin Mundt, Razvan Pascanu, Adrian Popescu, Andreas S. Tolias, Joost van de Weijer, Bing Liu, Vincenzo Lomonaco, Tinne Tuytelaars, Gido M. van de Ven:
Continual Learning: Applications and the Road Forward. CoRR abs/2311.11908 (2023) - 2022
- [j24]Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh C. Bongard, Andrew P. Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Reddy Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L. Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea Soltoggio, Nicholas Soures, Andreas S. Tolias, Darío Urbina-Meléndez, Francisco J. Valero Cuevas, Gido M. van de Ven, Joshua T. Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou, Hava T. Siegelmann:
Biological underpinnings for lifelong learning machines. Nat. Mach. Intell. 4(3): 196-210 (2022) - [c59]Hamed Fatemi Langroudi, Vedant Karia, Tej Pandit, Becky Mashaido, Dhireesha Kudithipudi:
ACTION: Automated Hardware-Software Codesign Framework for Low-precision Numerical Format SelecTION in TinyML. CoNGA 2022: 50-65 - [c58]Tej Pandit, Dhireesha Kudithipudi:
Low-Shot Learning and Pattern Separation using Cellular Automata Integrated CNNs. ICONS 2022: 17:1-17:9 - [c57]Vedant Karia, Fatima Tuz Zohora, Nicholas Soures, Dhireesha Kudithipudi:
SCOLAR: A Spiking Digital Accelerator with Dual Fixed Point for Continual Learning. ISCAS 2022: 1372-1376 - 2021
- [j23]Humza Syed, Ryan Bryla, Uttam K. Majumder, Dhireesha Kudithipudi:
Toward Near-Real-Time Training With Semi-Random Deep Neural Networks and Tensor-Train Decomposition. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 14: 8171-8179 (2021) - [c56]Hamed Fatemi Langroudi, Vedant Karia, Zachariah Carmichael, Abdullah M. Zyarah, Tej Pandit, John L. Gustafson, Dhireesha Kudithipudi:
ALPS: Adaptive Quantization of Deep Neural Networks With GeneraLized PositS. CVPR Workshops 2021: 3100-3109 - [c55]Fatima Tuz Zohora, Vedant Karia, Anurag Reddy Daram, Abdullah M. Zyarah, Dhireesha Kudithipudi:
MetaplasticNet: Architecture with Probabilistic Metaplastic Synapses for Continual Learning. ISCAS 2021: 1-5 - [i18]Hamed Fatemi Langroudi, Vedant Karia, Tej Pandit, Dhireesha Kudithipudi:
TENT: Efficient Quantization of Neural Networks on the tiny Edge with Tapered FixEd PoiNT. CoRR abs/2104.02233 (2021) - 2020
- [j22]Abdullah M. Zyarah, Kevin Gomez, Dhireesha Kudithipudi:
Neuromorphic System for Spatial and Temporal Information Processing. IEEE Trans. Computers 69(8): 1099-1112 (2020) - [c54]Hamed Fatemi Langroudi, Vedant Karia, John L. Gustafson, Dhireesha Kudithipudi:
Adaptive Posit: Parameter aware numerical format for deep learning inference on the edge. CVPR Workshops 2020: 3123-3131 - [c53]Fatima Tuz Zohora, Abdullah M. Zyarah, Nicholas Soures, Dhireesha Kudithipudi:
Metaplasticity in Multistate Memristor Synaptic Networks. ISCAS 2020: 1-5 - [c52]Sumin Jot, Abdullah M. Zyarah, Santosh Kurinec, Kai Ni, Fatima Tuz Zohora, Dhireesha Kudithipudi:
FeFET-Based Neuromorphic Architecture with On-Device Feedback Alignment Training. ISQED 2020: 317-322 - [c51]Tej Pandit, Dhireesha Kudithipudi:
Relational Neurogenesis for Lifelong Learning Agents. NICE 2020: 10:1-10:9 - [i17]Fatima Tuz Zohora, Abdullah M. Zyarah, Nicholas Soures, Dhireesha Kudithipudi:
Metaplasticity in Multistate Memristor Synaptic Networks. CoRR abs/2003.11638 (2020) - [i16]Abdullah M. Zyarah, Kevin Gomez, Dhireesha Kudithipudi:
End-to-End Memristive HTM System for Pattern Recognition and Sequence Prediction. CoRR abs/2006.11958 (2020)
2010 – 2019
- 2019
- [j21]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis. ACM J. Emerg. Technol. Comput. Syst. 15(3): 24:1-24:24 (2019) - [j20]Nicholas Soures, Dhireesha Kudithipudi:
Spiking Reservoir Networks: Brain-inspired recurrent algorithms that use random, fixed synaptic strengths. IEEE Signal Process. Mag. 36(6): 78-87 (2019) - [j19]Guang-Bin Huang, E. S. Eleftheriou, Dhireesha Kudithipudi, Jonathan Tapson, Hao Yu:
Guest Editorial: Special Issue on New Trends in Smart Chips and Smart Hardware. IEEE Trans. Emerg. Top. Comput. Intell. 3(1): 1-3 (2019) - [j18]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Neuromorphic Architecture for the Hierarchical Temporal Memory. IEEE Trans. Emerg. Top. Comput. Intell. 3(1): 4-14 (2019) - [c50]Zachariah Carmichael, Hamed Fatemi Langroudi, Char Khazanov, Jeffrey Lillie, John L. Gustafson, Dhireesha Kudithipudi:
Deep Positron: A Deep Neural Network Using the Posit Number System. DATE 2019: 1421-1426 - [c49]Zachariah Carmichael, Dhireesha Kudithipudi:
Stochastic Tucker-Decomposed Recurrent Neural Networks for Forecasting. GlobalSIP 2019: 1-5 - [c48]Hamed Fatemi Langroudi, Cory E. Merkel, Humza Syed, Dhireesha Kudithipudi:
Exploiting Randomness in Deep Learning Algorithms. IJCNN 2019: 1-8 - [c47]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Neuromemristive Multi-Layer Random Projection Network with On-Device Learning. IJCNN 2019: 1-8 - [c46]Anurag Reddy Daram, Dhireesha Kudithipudi, Angel Yanguas-Gil:
Task-Based Neuromodulation Architecture for Lifelong Learning. ISQED 2019: 191-197 - [i15]Zachariah Carmichael, Hamed Fatemi Langroudi, Char Khazanov, Jeffrey Lillie, John L. Gustafson, Dhireesha Kudithipudi:
Performance-Efficiency Trade-off of Low-Precision Numerical Formats in Deep Neural Networks. CoRR abs/1903.10584 (2019) - [i14]Hamed Fatemi Langroudi, Zachariah Carmichael, Dhireesha Kudithipudi:
Deep Learning Training on the Edge with Low-Precision Posits. CoRR abs/1907.13216 (2019) - [i13]Hamed Fatemi Langroudi, Zachariah Carmichael, David Pastuch, Dhireesha Kudithipudi:
Cheetah: Mixed Low-Precision Hardware & Software Co-Design Framework for DNNs on the Edge. CoRR abs/1908.02386 (2019) - [i12]Zachariah Carmichael, Humza Syed, Dhireesha Kudithipudi:
Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting. CoRR abs/1908.08380 (2019) - 2018
- [j17]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Semi-Trained Memristive Crossbar Computing Engine with In Situ Learning Accelerator. ACM J. Emerg. Technol. Comput. Syst. 14(4): 43:1-43:16 (2018) - [j16]Yang Cindy Yi, Dhireesha Kudithipudi:
Neuromorphic and cognitive computing and communication in hardware. Nano Commun. Networks 16: 10-11 (2018) - [c45]Anurag Reddy Daram, Karan Paluru, Vedant Karia, Dhireesha Kudithipudi:
Scalable IP Core for Feed Forward Random Networks. ELM 2018: 253-262 - [c44]Seyed Hamed Fatemi Langroudi, Tej Pandit, Dhireesha Kudithipudi:
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit. EMC2@ASPLOS 2018: 19-23 - [c43]Abdullah M. Zyarah, Nicholas Soures, Dhireesha Kudithipudi:
On-Device Learning in Memristor Spiking Neural Networks. ISCAS 2018: 1-5 - [c42]James Thesing, Dhireesha Kudithipudi:
Secure Neural Circuits to Mitigate Correlation Power Analysis on SHA-3 Hash Function. VLSID 2018: 161-166 - [i11]Qiuyi Wu, Ernest Fokoué, Dhireesha Kudithipudi:
On the Statistical Challenges of Echo State Networks and Some Potential Remedies. CoRR abs/1802.07369 (2018) - [i10]Seyed Hamed Fatemi Langroudi, Tej Pandit, Dhireesha Kudithipudi:
Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit. CoRR abs/1805.08624 (2018) - [i9]Zachariah Carmichael, Humza Syed, Stuart Burtner, Dhireesha Kudithipudi:
Mod-DeepESN: Modular Deep Echo State Network. CoRR abs/1808.00523 (2018) - [i8]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Neuromorphic Architecture for the Hierarchical Temporal Memory. CoRR abs/1808.05839 (2018) - [i7]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Semi-Trained Memristive Crossbar Computing Engine with In-Situ Learning Accelerator. CoRR abs/1808.07329 (2018) - [i6]Zachariah Carmichael, Seyed Hamed Fatemi Langroudi, Char Khazanov, Jeffrey Lillie, John L. Gustafson, Dhireesha Kudithipudi:
Deep Positron: A Deep Neural Network Using the Posit Number System. CoRR abs/1812.01762 (2018) - [i5]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Neuromemrisitive Architecture of HTM with On-Device Learning and Neurogenesis. CoRR abs/1812.10730 (2018) - 2017
- [j15]Nicholas Soures, Corey Merkel, Dhireesha Kudithipudi, Clare Thiem, Nathan R. McDonald:
Reservoir Computing in Embedded Systems: Three variants of the reservoir algorithm. IEEE Consumer Electron. Mag. 6(3): 67-73 (2017) - [j14]Cory E. Merkel, Dhireesha Kudithipudi, Manan Suri, Bryant T. Wysocki:
Stochastic CBRAM-Based Neuromorphic Time Series Prediction System. ACM J. Emerg. Technol. Comput. Syst. 13(3): 37:1-37:14 (2017) - [j13]Alex Pappachen James, Irina Fedorova, Timur Ibrayev, Dhireesha Kudithipudi:
HTM Spatial Pooler With Memristor Crossbar Circuits for Sparse Biometric Recognition. IEEE Trans. Biomed. Circuits Syst. 11(3): 640-651 (2017) - [j12]Haibo He, Robert Haas, Jun Fu, Barbara Hammer, Daniel W. C. Ho, Fakhri Karray, Dhireesha Kudithipudi, José Antonio Lozano, Teresa Bernarda Ludermir, Jacek Mandziuk, Stefano Melacci, Antonio Paiva, Hong Qiao, Alain Rakotomamonjy, Shiliang Sun, Johan A. K. Suykens, Meng Wang:
Editorial: A Successful Year and Looking Forward to 2017 and Beyond. IEEE Trans. Neural Networks Learn. Syst. 28(1): 2-7 (2017) - [c41]Dillon Graham, Seyed Hamed Fatemi Langroudi, Christopher Kanan, Dhireesha Kudithipudi:
Convolutional Drift Networks for Video Classification. ICRC 2017: 1-8 - [c40]Ernest Fokoué, Lakshmi Ravi, Dhireesha Kudithipudi:
A penalized maximum likelihood approach to the adaptive learning of the spatial pooler permanence. IJCNN 2017: 962-967 - [c39]Nicholas Soures, Lydia Hays, Dhireesha Kudithipudi:
Robustness of a memristor based liquid state machine. IJCNN 2017: 2414-2420 - [c38]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Extreme learning machine as a generalizable classification engine. IJCNN 2017: 3371-3376 - [c37]Abdullah M. Zyarah, Nicholas Soures, Lydia Hays, Robin Jacobs-Gedrim, Sapan Agarwal, Matthew J. Marinella, Dhireesha Kudithipudi:
Ziksa: On-chip learning accelerator with memristor crossbars for multilevel neural networks. ISCAS 2017: 1-4 - [c36]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Invited paper: Resource sharing in feed forward neural networks for energy efficiency. MWSCAS 2017: 543-546 - [c35]Nicholas Soures, Lydia Hays, Eric Bohannon, Abdullah M. Zyarah, Dhireesha Kudithipudi:
On-device STDP and synaptic normalization for neuromemristive spiking neural network. MWSCAS 2017: 1081-1084 - [c34]Swathika Ramakrishnan, Dhireesha Kudithipudi:
On accelerating stochastic neural networks. NANOCOM 2017: 16:1-16:5 - [i4]Dillon Graham, Seyed Hamed Fatemi Langroudi, Christopher Kanan, Dhireesha Kudithipudi:
Convolutional Drift Networks for Video Classification. CoRR abs/1711.01201 (2017) - 2016
- [j11]Cory E. Merkel, Raqibul Hasan, Nicholas Soures, Dhireesha Kudithipudi, Tarek M. Taha, Sapan Agarwal, Matthew J. Marinella:
Neuromemristive Systems: Boosting Efficiency through Brain-Inspired Computing. Computer 49(10): 56-64 (2016) - [j10]James Mnatzaganian, Ernest Fokoué, Dhireesha Kudithipudi:
A Mathematical Formalization of Hierarchical Temporal Memory's Spatial Pooler. Frontiers Robotics AI 3: 81 (2016) - [c33]Anvesh Polepalli, Nicholas Soures, Dhireesha Kudithipudi:
Digital neuromorphic design of a Liquid State Machine for real-time processing. ICRC 2016: 1-8 - [c32]Timur Ibrayev, Alex Pappachen James, Cory E. Merkel, Dhireesha Kudithipudi:
A design of HTM spatial pooler for face recognition using memristor-CMOS hybrid circuits. ISCAS 2016: 1254-1257 - [c31]Dan Christiani, Cory E. Merkel, Dhireesha Kudithipudi:
Invited: Towards a scalable neuromorphic hardware for classification and prediction with stochastic No-Prop algorithms. ISQED 2016: 124-128 - [c30]Anvesh Polepalli, Dhireesha Kudithipudi:
Reconfigurable Digital Design of a Liquid State Machine for Spatio-Temporal Data. NANOCOM 2016: 15:1-15:6 - [i3]James Mnatzaganian, Ernest Fokoué, Dhireesha Kudithipudi:
A Mathematical Formalization of Hierarchical Temporal Memory Cortical Learning Algorithm's Spatial Pooler. CoRR abs/1601.06116 (2016) - [i2]Cory E. Merkel, Dhireesha Kudithipudi:
Unsupervised Learning in Neuromemristive Systems. CoRR abs/1601.07482 (2016) - [i1]Lennard Streat, Dhireesha Kudithipudi, Kevin Gomez:
Non-volatile Hierarchical Temporal Memory: Hardware for Spatial Pooling. CoRR abs/1611.02792 (2016) - 2015
- [c29]Colin Donahue, Cory E. Merkel, Qutaiba Saleh, Levs Dolgovs, Yu Kee Ooi, Dhireesha Kudithipudi, Bryant T. Wysocki:
Design and analysis of neuromemristive echo state networks with limited-precision synapses. CISDA 2015: 1-6 - [c28]Qutaiba Saleh, Cory E. Merkel, Dhireesha Kudithipudi, Bryant T. Wysocki:
Memristive computational architecture of an echo state network for real-time speech-emotion recognition. CISDA 2015: 1-5 - [c27]Abdullah M. Zyarah, Dhireesha Kudithipudi:
Reconfigurable hardware architecture of the spatial pooler for hierarchical temporal memory. SoCC 2015: 143-153 - [c26]Cory E. Merkel, Dhireesha Kudithipudi:
Comparison of Off-Chip Training Methods for Neuromemristive Systems. VLSID 2015: 99-104 - 2014
- [j9]Cory E. Merkel, Dhireesha Kudithipudi:
Temperature Sensing RRAM Architecture for 3-D ICs. IEEE Trans. Very Large Scale Integr. Syst. 22(4): 878-887 (2014) - [c25]Cory E. Merkel, Qutaiba Saleh, Colin Donahue, Dhireesha Kudithipudi:
Memristive Reservoir Computing Architecture for Epileptic Seizure Detection. BICA 2014: 249-254 - [c24]Cory E. Merkel, Dhireesha Kudithipudi:
A current-mode CMOS/memristor hybrid implementation of an extreme learning machine. ACM Great Lakes Symposium on VLSI 2014: 241-242 - [c23]Cory E. Merkel, Dhireesha Kudithipudi:
Neuromemristive Extreme Learning Machines for Pattern Classification. ISVLSI 2014: 77-82 - [c22]Cory E. Merkel, Dhireesha Kudithipudi:
A stochastic learning algorithm for neuromemristive systems. SoCC 2014: 359-364 - [c21]Dhireesha Kudithipudi, Cory E. Merkel, Yu Kee Ooi, Qutaiba Saleh, Garrett S. Rose:
On designing circuit primitives for cortical processors with memristive hardware. SoCC 2014: 371-376 - [p3]Dhireesha Kudithipudi, Cory E. Merkel, Michael Soltiz, Garrett S. Rose, Robinson E. Pino:
Design of Neuromorphic Architectures with Memristors. Network Science and Cybersecurity 2014: 93-103 - [p2]Garrett S. Rose, Dhireesha Kudithipudi, Ganesh Khedkar, Nathan R. McDonald, Bryant T. Wysocki, Lok-Kwong Yan:
Nanoelectronics and Hardware Security. Network Science and Cybersecurity 2014: 105-123 - 2013
- [j8]Michael Soltiz, Dhireesha Kudithipudi, Cory E. Merkel, Garrett S. Rose, Robinson E. Pino:
Memristor-Based Neural Logic Blocks for Nonlinearly Separable Functions. IEEE Trans. Computers 62(8): 1597-1606 (2013) - [c20]Cory E. Merkel, Dhireesha Kudithipudi, Nick Sereni:
Periodic activation functions in memristor-based analog neural networks. IJCNN 2013: 1-7 - [p1]Dhireesha Kudithipudi, Qinru Qiu, Ayse K. Coskun:
Thermal Management in Many Core Systems. Evolutionary Based Solutions for Green Computing 2013: 161-185 - 2012
- [c19]David Brenner, Cory E. Merkel, Dhireesha Kudithipudi:
Design-time performance evaluation of thermal management policies for SRAM and RRAM based 3D MPSoCs. ACM Great Lakes Symposium on VLSI 2012: 177-182 - [c18]Dhireesha Kudithipudi, Ayse K. Coskun, Sherief Reda, Qinru Qiu:
Temperature-aware computing: Achievements and remaining challenges. IGCC 2012: 1-3 - [c17]Ganesh Khedkar, Dhireesha Kudithipudi:
RRAM Motifs for Mitigating Differential Power Analysis Attacks (DPA). ISVLSI 2012: 88-93 - [c16]Michael Soltiz, Cory E. Merkel, Dhireesha Kudithipudi, Garrett S. Rose:
RRAM-based adaptive neural logic block for implementing non-linearly separable functions in a single layer. NANOARCH 2012: 218-225 - [c15]Matthew Catanzaro, Dhireesha Kudithipudi:
Reconfigurable RRAM for LUT logic mapping: A case study for reliability enhancement. SoCC 2012: 94-99 - [c14]Cory E. Merkel, Dhireesha Kudithipudi, Andres Kwasinski:
Lightweight energy prediction framework for solar-powered wireless sensor networks. SoCC 2012: 131-136 - [c13]Cory E. Merkel, Dhireesha Kudithipudi:
Towards Thermal Profiling in CMOS/Memristor Hybrid RRAM Architectures. VLSI Design 2012: 167-172 - [r1]J. Kevin Hicks, Dhireesha Kudithipudi:
Subthreshold Computing. Handbook of Energy-Aware and Green Computing 2012: 3-20 - 2011
- [j7]Pradeep S. Nair, Dhireesha Kudithipudi, Eugene B. John, Fred W. Hudson:
Execution characteristics of embedded applications on a Pentium 4-based personal computer. J. Embed. Comput. 4(3-4): 107-116 (2011) - [j6]J. Kevin Hicks, Dhireesha Kudithipudi:
Hybrid Subthreshold and Nearthreshold Design Methodology for Energy Minimization. J. Low Power Electron. 7(2): 172-184 (2011) - [c12]Satish G. Kandlikar, Dhireesha Kudithipudi, Carlos A. Rubio-Jimenez:
Cooling mechanisms in 3D ICs: Thermo-mechanical perspective. IGCC 2011: 1-8 - [c11]Cory E. Merkel, Nakul Nagpal, Sindhura Mandalapu, Dhireesha Kudithipudi:
Reconfigurable N-level memristor memory design. IJCNN 2011: 3042-3048 - 2010
- [j5]Sreeharsha Tavva, Dhireesha Kudithipudi:
Characterization of Variation Aware Nanoscale Static Random Access Memory Designs. J. Low Power Electron. 6(1): 56-65 (2010) - [c10]Sreeharsha Tavva, Dhireesha Kudithipudi:
Variation tolerant 9T SRAM cell design. ACM Great Lakes Symposium on VLSI 2010: 55-60 - [c9]Sumanth Amarchinta, Dhireesha Kudithipudi:
Performance enhancement of subthreshold circuits using substrate biasing and charge-boosting buffers. ACM Great Lakes Symposium on VLSI 2010: 369-372 - [c8]Andres Kwasinski, Dhireesha Kudithipudi:
Towards integrated circuit thermal profiling for reduced power consumption: Evaluation of distributed sensing techniques. Green Computing Conference 2010: 503-508 - [c7]Sumanth Amarchinta, Dhireesha Kudithipudi:
Ultra low energy standard cell design optimization for performance and placement algorithm. Green Computing Conference 2010: 509-517
2000 – 2009
- 2009
- [j4]Dhireesha Kudithipudi, Eugene John:
Implications of gated-Vss technique on leakage power in embedded caches. Int. J. Embed. Syst. 4(1): 17-26 (2009) - [c6]Pradeep S. Nair, Santosh Koppa, Eugene B. John, Dhireesha Kudithipudi:
Topology selection of FPGA look-up tables for low-leakage operation. ECCTD 2009: 73-76 - 2008
- [j3]Dhireesha Kudithipudi, Stefan Petko, Eugene B. John:
Caches for Multimedia Workloads: Power and Energy Tradeoffs. IEEE Trans. Multim. 10(6): 1013-1021 (2008) - [c5]Srikanth Katrue, Dhireesha Kudithipudi:
GALEOR: Leakage reduction for CMOS circuits. ICECS 2008: 574-577 - 2007
- [j2]Dhireesha Kudithipudi, Eugene John:
Static Power Analysis and Estimation in Ternary Content Addressable Memory Cells. J. Low Power Electron. 3(3): 293-301 (2007) - 2006
- [c4]Pradeep S. Nair, Dhireesha Kudithipudi, Eugene John, Fred W. Hudson:
Performance Analysis of Embedded Applications on a Pentium-4 Based Machine. ESA 2006: 191-197 - 2005
- [j1]Dhireesha Kudithipudi, Eugene John:
Implementation of Low Power Digital Multipliers using 10 -Transistor Adder Blocks. J. Low Power Electron. 1(3): 286-296 (2005) - [c3]Dhireesha Kudithipudi, Eugene John:
Parametrical characterization of leakage power in embedded system caches using gated-VSS. Circuits, Signals, and Systems 2005: 308-312 - 2004
- [c2]Dhireesha Kudithipudi, R. Kotha, Eugene John, Z. Pantic-Tanner:
Impact of nanotechnology on the performance of CMOS digital multipliers. Circuits, Signals, and Systems 2004: 439-442 - 2002
- [c1]Stefan Petko, Dhireesha Kudithipudi, Eugene John:
Cache performance of video computation workloads. DCV 2002: 169-175
Coauthor Index
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