Mapping of image and network processing tasks on high-throughput CMOL FPGA circuits. - Advait Madhavan; Dmitri B. Strukov (VLSI-SoC, 2012)

Race Logic: A hardware acceleration for dynamic programming algorithms. - Advait Madhavan; Timothy Sherwood; Dmitri B. Strukov (ISCA, 2014)

A configurable CMOS memory platform for 3D-integrated memristors. - Melika Payvand; Advait Madhavan; Miguel Angel Lastras-Montaño; Amirali Ghofrani; Justin Rofeh; Kwang-Ting Cheng; Dmitri B. Strukov; Luke Theogarajan (ISCAS, 2015)

Race Logic: Abusing Hardware Race Conditions to Perform Useful Computation. - Advait Madhavan; Timothy Sherwood; Dmitri B. Strukov (IEEE Micro, 2015)

Energy efficient computation with asynchronous races. - Advait Madhavan; Timothy Sherwood; Dmitri B. Strukov (DAC, 2016)

Abusing Hardware Race Conditions for High Throughput Energy Efficient Computation. - Advait Madhavan (2016)

A 4-mm2 180-nm-CMOS 15-Giga-cell-updates-per-second DNA sequence alignment engine based on asynchronous race conditions. - Advait Madhavan; Timothy Sherwood; Dmitri B. Strukov (CICC, 2017)

High-Throughput Pattern Matching With CMOL FPGA Circuits: Case for Logic-in-Memory Computing. - Advait Madhavan; Tim Sherwood; Dmitri B. Strukov (IEEE Trans. Very Large Scale Integr. Syst., 2018)

Boosted Race Trees for Low Energy Classification. - Georgios Tzimpragos; Advait Madhavan; Dilip Vasudevan; Dmitri B. Strukov; Timothy Sherwood (ASPLOS, 2019)

Streaming Batch Eigenupdates for Hardware Neuromorphic Networks. - Brian D. Hoskins; Matthew W. Daniels; Siyuan Huang 002; Advait Madhavan; Gina C. Adam; Nikolai Zhitenev; Jabez J. McClelland; Mark D. Stiles (CoRR, 2019)

Energy-efficient stochastic computing with superparamagnetic tunnel junctions. - Matthew W. Daniels; Advait Madhavan; Philippe Talatchian; Alice Mizrahi; Mark D. Stiles (CoRR, 2019)

A Computational Temporal Logic for Superconducting Accelerators. - Georgios Tzimpragos; Dilip Vasudevan; Nestan Tsiskaridze; George Michelogiannakis; Advait Madhavan; Jennifer Volk; John Shalf; Timothy Sherwood (ASPLOS, 2020)

Lessons Learned the Hard Way. - Tobi Delbruck; Ibrahim Abe M. Elfadel; Shahzad Muzaffar; Germain Haessig; Bo Wang 012; Amine Bermak; Rui Graca; Luis A. Camuñas-Mesa; Bathiya Senevirathna; Pamela Abshire; Bernabé Linares-Barranco; Saeed Afshar; et al. (ISCAS, 2020)

Storing and Retrieving Wavefronts with Resistive Temporal Memory. - Advait Madhavan; Mark D. Stiles (ISCAS, 2020)

Temporal Memory with Magnetic Racetracks. - Hamed Vakili; Mohammad Nazmus Sakib; Samiran Ganguly; Mircea Stan; Matthew W. Daniels; Advait Madhavan; Mark D. Stiles; Avik W. Ghosh (CoRR, 2020)

A System for Validating Resistive Neural Network Prototypes. - Brian Hoskins; Wen Ma; Mitchell Fream; Osama Yousuf; Matthew W. Daniels; Jonathan Goodwill; Advait Madhavan; Hoang Tung; Mark Branstad; Muqing Liu; Rasmus Madsen; Jabez J. McClelland; et al. (ICONS, 2021)

In-sensor classification with boosted race trees. - Georgios Tzimpragos; Advait Madhavan; Dilip Vasudevan; Dmitri B. Strukov; Timothy Sherwood (Commun. ACM, 2021)

Mutual control of stochastic switching for two electrically coupled superparamagnetic tunnel junctions. - Philippe Talatchian; Matthew W. Daniels; Advait Madhavan; Matthew R. Pufall; Emilie Jué; William H. Rippard; Jabez J. McClelland; Mark D. Stiles (CoRR, 2021)

Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions. - Jonathan M. Goodwill; Nitin Prasad; Brian D. Hoskins; Matthew W. Daniels; Advait Madhavan; Lei Wan; Tiffany S. Santos; Michael Tran; Jordan A. Katine; Patrick M. Braganca; Mark D. Stiles; Jabez J. McClelland (CoRR, 2021)

Temporal State Machines: Using Temporal Memory to Stitch Time-based Graph Computations. - Advait Madhavan; Matthew W. Daniels; Mark D. Stiles (ACM J. Emerg. Technol. Comput. Syst., 2021)

Temporal Computing With Superconductors. - Georgios Tzimpragos; Jennifer Volk; Dilip Vasudevan; Nestan Tsiskaridze; George Michelogiannakis; Advait Madhavan; John Shalf; Timothy Sherwood (IEEE Micro, 2021)

Associative memories using complex-valued Hopfield networks based on spin-torque oscillator arrays. - Nitin Prasad; Prashansa Mukim; Advait Madhavan; Mark D. Stiles (Neuromorph. Comput. Eng., 2022)

Experimental demonstration of a robust training method for strongly defective neuromorphic hardware. - William A. Borders; Advait Madhavan; Matthew W. Daniels; Vasileia Georgiou; Martin Lueker-Boden; Tiffany S. Santos; Patrick M. Braganca; Mark D. Stiles; Jabez J. McClelland; Brian D. Hoskins (CoRR, 2023)

Programmable electrical coupling between stochastic magnetic tunnel junctions. - Sidra Gibeault; Temitayo N. Adeyeye; Liam A. Pocher; Daniel P. Lathrop; Matthew W. Daniels; Mark D. Stiles; Jabez J. McClelland; William A. Borders; Jason T. Ryan; Philippe Talatchian; Ursula Ebels; Advait Madhavan (CoRR, 2023)

Energy Efficient Convolutions with Temporal Arithmetic. - Rhys Gretsch; Peiyang Song 002; Advait Madhavan; Jeremy Lau; Timothy Sherwood (ASPLOS (2), 2024)

Exploration of the viability of TiN/TiOX ReRAM in Computational Random-Access Memory (CRAM). - Brandon R. Zink; Advait Madhavan; William A. Borders; Jabez J. McClelland (ICRC, 2024)

Layer Ensemble Averaging for Improving Memristor-Based Artificial Neural Network Performance. - Osama Yousuf; Brian Hoskins; Karthick Ramu; Mitchell Fream; William A. Borders; Advait Madhavan; Matthew W. Daniels; Andrew M. Dienstfrey; Jabez J. McClelland; Martin Lueker-Boden; Gina C. Adam (CoRR, 2024)

Sampling from exponential distributions in the time domain with superparamagnetic tunnel junctions. - Temitayo N. Adeyeye; Sidra Gibeault; Daniel P. Lathrop; Matthew W. Daniels; Mark D. Stiles; Jabez J. McClelland; William A. Borders; Jason T. Ryan; Philippe Talatchian; Ursula Ebels; Advait Madhavan (CoRR, 2024)