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Publications

Neuromorphic Systems


N Soures*, A Zyarah*, D Kudithipudi (2018). Enabling on-Device Learning with Deep Spiking Neural Networks for Video Classification. Meeting Abstracts, ECS Transactions. Retrieved from: http://ma.ecsdl.org/content/MA2018-01/22/1380.short

N Soures*, D Kudithipudi, RB Jacobs-Gedrim, S Agarwal, M Marinella (2018). Enabling On-Device Learning with Deep Spiking Neural Networks for Speech Recognition. ECS Transactions. Retrieved from: http://ecst.ecsdl.org/content/85/6/127.short

YC Yi, D Kudithipudi (2018). Neuromorphic and cognitive computing and communication in hardware. Nano communication networks, Elsevier. Retrieved from: https://www.sciencedirect.com/science/article/pii/S1878778918300152

J Thesing*, D Kudithipudi (2018). Secure Neural Circuits to Mitigate Correlation Power Analysis on SHA-3 Hash Function. VLSI Design and 2018 17th International Conference on Embedded Systems (VLSID). Retrieved from: https://ieeexplore.ieee.org/abstract/document/8326918/

AP James, I Fedorova, T Ibrayev, D Kudithipudi (2017). HTM spatial pooler with memristor crossbar circuits for sparse biometric recognition. IEEE transactions on biomedical circuits and systems 11 (3), 640-651. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7865953/

Abdullah M Zyarah*, Nicholas Soures*, Lydia Hays*, Robin B Jacobs-Gedrim, Sapan Agarwal, Matthew Marinella, Dhireesha Kudithipudi (2017). Ziksa: On-chip learning accelerator with memristor crossbars for multilevel neural networks. Circuits and Systems (ISCAS), 2017 IEEE International Symposium on. Retrieved from: https://ieeexplore.ieee.org/abstract/document/8050531/

N Soures*, L Hays*, D Kudithipudi (2017). Robustness of a memristor based liquid state machine. Neural Networks (IJCNN), 2017 International Joint Conference on, 2414-2420. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7966149/

C Merkel, D Kudithipudi, M Suri, B Wysocki (2017). Stochastic CBRAM-Based Neuromorphic Time Series Prediction System. ACM Journal on Emerging Technologies in Computing Systems (JETC). Retrieved from: https://dl.acm.org/citation.cfm?id=2996193

N Soures*, L Hays*, E Bohannon, AM Zyarah, D Kudithipudi (2017). On-Device STDP and Synaptic Normalization for Neuromemristive Spiking Neural Network. Circuits and Systems (MWSCAS), 2017 IEEE International Midwest Symposium on. IEEE. Retrieved from: https://ieeexplore.ieee.org/iel7/8039346/8052834/08053115.pdf

C Merkel, D Kudithipudi (2017). Neuromemristive systems: A circuit design perspective. Advances in Neuromorphic Hardware Exploiting Emerging Nanoscale Devices. Retrieved from: https://link.springer.com/chapter/10.1007/978-81-322-3703-7_3 

C Merkel, D Kudithipudi (2016). Method and Apparatus for Training Memristive Learning Systems. US Patent App. 15/159,949. Retrieved from: https://patents.google.com/patent/US20160342904A1/en 

L Streat*, D Kudithipudi, K Gomez (2016). Non-volatile hierarchical temporal memory: Hardware for spatial pooling. arXiv preprint arXiv:1611.02792. Retrieved from: https://arxiv.org/abs/1611.02792 

A Polepalli*, N Soures*, D Kudithipudi (2016). Reconfigurable digital design of a liquid state machine for spatio-temporal data. Proceedings of the 3rd ACM International Conference on Nanoscale Computing and Communication. Rebooting Computing (ICRC), IEEE International Conference on, 1-8. Retrieved from: https://dl.acm.org/citation.cfm?id=2967449

Cory Merkel, Raqibul Hasan, Nicholas Soures*, Dhireesha Kudithipudi, Tarek Taha, Sapan Agarwal, Matthew Marinella (2016). Neuromemristive systems: Boosting efficiency through brain-inspired computing. Computer (IEEE). Retrieved from: https://www.computer.org/csdl/mags/co/2016/10/mco2016100056-abs.html 

Timur Ibrayev, Alex Pappachen James, Cory Merkel, Dhireesha Kudithipudi (2016). A design of HTM spatial pooler for face recognition using Memristor-CMOS hybrid circuits. 2016 IEEE International Symposium on Circuits and Systems (ISCAS),. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7527475/ 

Dan Christiani, Cory Merkel, Dhireesha Kudithipudi (2016). Towards a scalable neuromorphic hardware for classification and prediction with stochastic no-prop algorithms. 2016 17th International Symposium on Quality Electronic Design (ISQED),. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7479187/

Dhireesha Kudithipudi, Qutaiba Saleh*, Cory Merkel*, James Thesing*, Bryant Wysocki* (2016). Design and analysis of a neuromemristive reservoir computing architecture for biosignal processing. Frontiers in neuroscience. Retrieved from: https://www.frontiersin.org/articles/10.3389/fnins.2015.00502 

C Merkel*, D Kudithipudi (2016). Unsupervised learning in neuromemristive systems. arXiv preprint arXiv:1601.07482. Retrieved from: https://arxiv.org/abs/1601.07482 

AM Zyarah*, D Kudithipudi (2015). Reconfigurable hardware architecture of the spatial pooler for hierarchical temporal memory. System-on-Chip Conference (SOCC), 2015 28th IEEE International. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7406930/ 

Qutaiba Saleh, Cory Merkel, Dhireesha Kudithipudi, Bryant Wysocki (2015). Memristive computational architecture of an echo state network for real-time speech-emotion recognition. IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2015. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7208624/ 

Colin Donahue*, Cory Merkel*, Qutaiba Saleh*, Levs Dolgovs, Yu Kee Ooi, Dhireesha Kudithipudi, Bryant Wysocki*  (2015). Design and analysis of neuromemristive echo state networks with limited-precision synapses. IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2015. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7208623/

C Merkel*, D Kudithipudi (2015). Comparison of off-chip training methods for neuromemristive systems. VLSI Design (VLSID), 2015 28th International Conference on, 99-104. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7031715/

C Merkel*, D Kudithipudi (2014). A stochastic learning algorithm for neuromemristive systems. System-on-Chip Conference (SOCC), 2014 27th IEEE International, 359-364. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6948954/

D Kudithipudi, C Merkel*, YK Ooi*, Q Saleh*, GS Rose (2014). On designing circuit primitives for cortical processors with memristive hardware. System-on-Chip Conference (SOCC), 2014 27th IEEE International, 359-364. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6948957/ 

C Merkel*, D Kudithipudi (2014). Neuromemristive extreme learning machines for pattern classification. VLSI (ISVLSI), 2014 IEEE Computer Society Annual Symposium on, 77-82. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6903339/

C Merkel*, D Kudithipudi, R Ptucha (2014). Heterogeneous cmos/memristor hardware neural networks for real-time target classification. Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII. Retrieved from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9119/9...

G Khedkar, C Donahue, D Kudithipudi (2014). Towards leakage resiliency: memristor-based AES design for differential power attack mitigation. Machine Intelligence and Bio-inspired Computation: Theory and Applications VIII. Retrieved from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9119/9...

C Merkel*, D Kudithipudi (2014). A current-mode CMOS/memristor hybrid implementation of an extreme learning machine. Proceedings of the 24th edition of the Great Lakes Symposium on VLSI. Retrieved from: https://dl.acm.org/citation.cfm?id=2591572

Cory Merkel, Qutaiba Saleh, Colin Donahue, Dhireesha Kudithipudi (2014). Memristive reservoir computing architecture for epileptic seizure detection. Procedia Computer Science. Retrieved from: https://www.sciencedirect.com/science/article/pii/S1877050914015555 

D Kudithipudi, C Merkel*, M Soltiz*, GS Rose, RE Pino (2014). Design of neuromorphic architectures with memristors. Network Science and Cybersecurity, 105-123. Retrieved from: https://link.springer.com/chapter/10.1007/978-1-4614-7597-2_6 

C Merkel*, D Kudithipudi, N Sereni* (2013). Periodic activation functions in memristor-based analog neural networks. Neural Networks (IJCNN). Retrieved from: https://ieeexplore.ieee.org/abstract/document/6706772/ 

M Soltiz*, D Kudithipudi, C Merkel*, GS Rose, RE Pino (2013). Memristor-based neural logic blocks for nonlinearly separable functions. IEEE Transactions on computers 62 (8), 1597-1606. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6489976/

M Soltiz*, C Merkel*, D Kudithipudi, GS Rose (2012). RRAM-based adaptive neural logic block for implementing non-linearly separable functions in a single layer. Proceedings of the 2012 IEEE/ACM International Symposium on Nanoscale Architectures. Retrieved from: https://dl.acm.org/citation.cfm?id=2765530 

Brain-Inspired Algorithms


Q Wu*, E Fokoue, D Kudithipudi (2018). On the Statistical Challenges of Echo State Networks and Some Potential Remedies. arXiv preprint arXiv:1802.07369. Retrieved from: https://arxiv.org/abs/1802.07369

Nicholas Soures*, Abdullah Zyarah*, Kristofor David Carlson, James Bradley Aimone, Dhireesha Kudithipudi (2017). How Neural Plasticity Boosts Performance of Spiking Neural Networks. Sandia National Lab.(SNL-NM). Retrieved from: https://www.osti.gov/servlets/purl/1458069

E Fokoue, L Ravi, D Kudithipudi (2017). A penalized maximum likelihood approach to the adaptive learning of the spatial pooler permanence. Neural Networks (IJCNN), 2017 International Joint Conference on, 962-967. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7965956/

N Soures*, C Merkel, D Kudithipudi, C Thiem, N McDonald (2017). Reservoir Computing in Embedded Systems: Three variants of the reservoir algorithm. IEEE Consumer Electronics Magazine 6 (3), 67-73. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7948705/

D Graham*, SHF Langroudi*, C Kanan, D Kudithipudi (2017). Convolutional Drift Networks for Video Classification. Rebooting Computing (ICRC), 2017 IEEE International Conference on, 1-8. Retrieved from: https://ieeexplore.ieee.org/abstract/document/8123647/

J Mnatzaganian*, E Fokoué, D Kudithipudi (2017). A mathematical formalization of hierarchical temporal memory's spatial pooler. Frontiers in Robotics and AI. Retrieved from: https://www.frontiersin.org/articles/10.3389/frobt.2016.00081

Energy-Efficient Machine Intelligence


Z. Carmichael*, H. F. Langroudi*, C. Khazanov*, J. Lillie, J. L. Gustafson, D. Kudithipudi (2019). Deep Positron: A Deep Neural Network Using Posit Number System. Proceedings of the IEEE Conference and Exhibition on Design, Automation and Test in Europe (DATE). Florence, Italy.

SHF Langroudi*, T Pandit*, D Kudithipudi (2018). Deep Learning Inference on Embedded Devices: Fixed-Point vs Posit. arXiv preprint arXiv:1805.08624. Retrieved from: https://arxiv.org/abs/1805.08624

S Ramakrishnan*, D Kudithipudi (2017). On accelerating stochastic neural networks. Proceedings of the 4th ACM International Conference on Nanoscale Computing and Communication. Retrieved from: https://dl.acm.org/citation.cfm?id=3123959

N Soures*, C Merkel, D Kudithipudi, C Thiem, N McDonald (2017). Reservoir Computing in Embedded Systems: Three variants of the reservoir algorithm. IEEE Consumer Electronics Magazine 6 (3), 67-73. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7948705/

AM Zyarah*, D Kudithipudi (2017). Extreme learning machine as a generalizable classification engine. Neural Networks (IJCNN), 2017 International Joint Conference on, 3371-3376. Retrieved from: https://ieeexplore.ieee.org/abstract/document/7966279/

Abdullah M Zyarah*, Abhishek Ramesh*, Cory Merkel, Dhireesha Kudithipudi (2016). Optimized hardware framework of MLP with random hidden layers for classification applications. Machine Intelligence and Bio-inspired Computation: Theory and Applications X. Retrieved from: https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9850/9...

Low-Power Hardware


C Merkel*, D Kudithipudi (2014). Thermal management apparatuses with temperature sensing resistive random access memory devices and methods thereof. US Patent 8,750,065. Retrieved from: https://patents.google.com/patent/US8750065B2/en 

G Khedkar, D Kudithipudi, GS Rose(2015). Power profile obfuscation using nanoscale memristive devices to counter DPA attacks. IEEE Transactions on Nanotechnology 14 (1), 26-35. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6918465/

CE Merkel*, D Kudithipudi (2014). Temperature sensing rram architecture for 3-d ics. IEEE Transactions on Very Large Scale Integration (VLSI) Systems 22 (4), 878-887. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6515635/

GS Rose, D Kudithipudi, G Khedkar, N McDonald, B Wysocki, LK Yan (2014). Nanoelectronics and hardware security. Network Science and Cybersecurity, 105-123. Retrieved from: https://link.springer.com/10.1007/978-1-4614-7597-2_7

S Mohanram*, D Brenner*,  D Kudithipudi (2013). Hierarchical optimization of tsv placement with inter-tier liquid cooling in 3d-ic mpsocs. 29th IEEE Semiconductor Thermal Measurement and Management Symposium. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6526798/

D Kudithipudi, Q Qu, AK Coskun (2013). Thermal management in many core systems. Evolutionary Based Solutions for Green Computing, 161-185. Retrieved from: https://link.springer.com/chapter/10.1007/978-3-642-30659-4_6 

CE Merkel, D Kudithipudi, A Kwasinski (2012). Lightweight energy prediction framework for solar-powered wireless sensor networks. SOC Conference (SOCC), 2012 IEEE International, 131-136. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6398397/ 

M Catanzaro, D Kudithipudi (2012). Reconfigurable rram for lut logic mapping: A case study for reliability enhancement. SOC Conference (SOCC), 2012 IEEE International, 94-99. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6398384/

G Khedkar, D Kudithipudi (2012). RRAM motifs for mitigating differential power analysis attacks (DPA). Retrieved from: https://www.computer.org/csdl/proceedings/isvlsid/2012/4767/00/4767a088-...

D Kudithipudi, A Coskun, S Reda, Q Qiu (2012). Temperature-aware computing: Achievements and remaining challenges. Green Computing Conference (IGCC), 2012 International, 1-3. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6322291/

D Brenner*, C Merkel*, D Kudithipudi (2012). Design-time performance evaluation of thermal management policies for SRAM and RRAM based 3D MPSoCs. Proceedings of the great lakes symposium on VLSI, 177-182. Retrieved from: https://dl.acm.org/citation.cfm?id=2206824

D Kudithipudi, JK Hicks (2012). Subthreshold Computing. Handbook of Energy-Aware and Green Computing, Volume 1, 21-38. Retrieved from: https://www.taylorfrancis.com/books/e/9781439850411/chapters/10.1201%2Fb...

CE Merkel, D Kudithipudi (2012). Towards thermal profiling in CMOS/memristor hybrid RRAM architectures. VLSI Design (VLSID), 2012 25th International Conference on, 167-172. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6167747/

D Kudithipudi, CE Merkel* (2012). Reconfigurable memristor fabrics for heterogeneous computing. Advances in Neuromorphic Memristor Science and Applications, 89-106. Retrieved from: https://link.springer.com/10.1007/978-94-007-4491-2_7

CE Merkel, N Nagpal, S Mandalapu, D Kudithipudi (2011). Reconfigurable N-level memristor memory design. Neural Networks (IJCNN), The 2011 International Joint Conference on, 3042-3048. Retrieved from: https://ieeexplore.ieee.org/abstract/document/6033622/

SG Kandlikar, D Kudithipudi, CA Rubio-Jimenez (2011). Cooling mechanisms in 3D ICs: Thermo-mechanical perspective. IEEE. Retrieved from: https://www.computer.org/csdl/proceedings/igcc/2011/1222/00/06008573-abs...

JK Hicks, D Kudithipudi (2011). Hybrid subthreshold and nearthreshold design methodology for energy minimization. Journal of Low Power Electronics. Retrieved from: https://www.ingentaconnect.com/content/asp/jolpe/2011/00000007/00000002/...

A Kwasinski, D Kudithipudi (2010). Towards integrated circuit thermal profiling for reduced power consumption: Evaluation of distributed sensing techniques. Green Computing Conference, 2010 International, 503-508. Retrieved from: https://ieeexplore.ieee.org/abstract/document/5598276/

S Amarchinta, D Kudithipudi (2010). Ultra low energy standard cell design optimization for performance and placement algorithm. Green Computing Conference, 2010 International, 509-517 . Retrieved from: https://ieeexplore.ieee.org/abstract/document/5598273/

S Amarchinta, D Kudithipudi (2010). Performance enhancement of subthreshold circuits using substrate biasing and charge-boosting buffers. Proceedings of the 20th symposium on Great lakes symposium on VLSI, 369-372. Retrieved from: https://dl.acm.org/citation.cfm?id=1785566

S Tavva, D Kudithipudi (2010). Variation tolerant 9T SRAM cell design. Proceedings of the 20th symposium on Great lakes symposium on VLSI, 55-60. Retrieved from: https://dl.acm.org/citation.cfm?id=1785495

S Tavva, D Kudithipudi (2010). Characterization of variation aware nanoscale static random access memory designs. Journal of Low Power Electronics. Retrieved from: https://www.ingentaconnect.com/content/asp/jolpe/2010/00000006/00000001/...

Last updated August 20, 2018