Zobrazeno 1 - 10
of 229
pro vyhledávání: '"Strukov, Dmitri"'
Autor:
Mehonic, Adnan, Ielmini, Daniele, Roy, Kaushik, Mutlu, Onur, Kvatinsky, Shahar, Serrano-Gotarredona, Teresa, Linares-Barranco, Bernabe, Spiga, Sabina, Savelev, Sergey, Balanov, Alexander G, Chawla, Nitin, Desoli, Giuseppe, Malavena, Gerardo, Compagnoni, Christian Monzio, Wang, Zhongrui, Yang, J Joshua, Syed, Ghazi Sarwat, Sebastian, Abu, Mikolajick, Thomas, Noheda, Beatriz, Slesazeck, Stefan, Dieny, Bernard, Tuo-Hung, Hou, Varri, Akhil, Bruckerhoff-Pluckelmann, Frank, Pernice, Wolfram, Zhang, Xixiang, Pazos, Sebastian, Lanza, Mario, Wiefels, Stefan, Dittmann, Regina, Ng, Wing H, Buckwell, Mark, Cox, Horatio RJ, Mannion, Daniel J, Kenyon, Anthony J, Lu, Yingming, Yang, Yuchao, Querlioz, Damien, Hutin, Louis, Vianello, Elisa, Chowdhury, Sayeed Shafayet, Mannocci, Piergiulio, Cai, Yimao, Sun, Zhong, Pedretti, Giacomo, Strachan, John Paul, Strukov, Dmitri, Gallo, Manuel Le, Ambrogio, Stefano, Valov, Ilia, Waser, Rainer
The roadmap is organized into several thematic sections, outlining current computing challenges, discussing the neuromorphic computing approach, analyzing mature and currently utilized technologies, providing an overview of emerging technologies, add
Externí odkaz:
http://arxiv.org/abs/2407.02353
In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) emphasizing on integrating dual information streams within the neocortical pyramidal neurons specifically Context- Dependent Leaky
Externí odkaz:
http://arxiv.org/abs/2404.18066
Autor:
Dobrynin, Dmitrii, Renaudineau, Adrien, Hizzani, Mohammad, Strukov, Dmitri, Mohseni, Masoud, Strachan, John Paul
Physics-based Ising machines (IM) have been developed as dedicated processors for solving hard combinatorial optimization problems with higher speed and better energy efficiency. Generally, such systems employ local search heuristics to traverse ener
Externí odkaz:
http://arxiv.org/abs/2403.01320
Ising Machine is a promising computing approach for solving combinatorial optimization problems. It is naturally suited for energy-saving and compact in-memory computing implementations with emerging memories. A na\"ive in-memory computing implementa
Externí odkaz:
http://arxiv.org/abs/2401.16202
Autor:
Hizzani, Mohammad, Heittmann, Arne, Hutchinson, George, Dobrynin, Dmitrii, Van Vaerenbergh, Thomas, Bhattacharya, Tinish, Renaudineau, Adrien, Strukov, Dmitri, Strachan, John Paul
Publikováno v:
2024 IEEE International Symposium on Circuits and Systems (ISCAS)
Ising solvers offer a promising physics-based approach to tackle the challenging class of combinatorial optimization problems. However, typical solvers operate in a quadratic energy space, having only pair-wise coupling elements which already dominat
Externí odkaz:
http://arxiv.org/abs/2311.01171
Autor:
Deiana, Allison McCarn, Tran, Nhan, Agar, Joshua, Blott, Michaela, Di Guglielmo, Giuseppe, Duarte, Javier, Harris, Philip, Hauck, Scott, Liu, Mia, Neubauer, Mark S., Ngadiuba, Jennifer, Ogrenci-Memik, Seda, Pierini, Maurizio, Aarrestad, Thea, Bahr, Steffen, Becker, Jurgen, Berthold, Anne-Sophie, Bonventre, Richard J., Bravo, Tomas E. Muller, Diefenthaler, Markus, Dong, Zhen, Fritzsche, Nick, Gholami, Amir, Govorkova, Ekaterina, Hazelwood, Kyle J, Herwig, Christian, Khan, Babar, Kim, Sehoon, Klijnsma, Thomas, Liu, Yaling, Lo, Kin Ho, Nguyen, Tri, Pezzullo, Gianantonio, Rasoulinezhad, Seyedramin, Rivera, Ryan A., Scholberg, Kate, Selig, Justin, Sen, Sougata, Strukov, Dmitri, Tang, William, Thais, Savannah, Unger, Kai Lukas, Vilalta, Ricardo, Krosigk, Belinavon, Warburton, Thomas K., Flechas, Maria Acosta, Aportela, Anthony, Calvet, Thomas, Cristella, Leonardo, Diaz, Daniel, Doglioni, Caterina, Galati, Maria Domenica, Khoda, Elham E, Fahim, Farah, Giri, Davide, Hawks, Benjamin, Hoang, Duc, Holzman, Burt, Hsu, Shih-Chieh, Jindariani, Sergo, Johnson, Iris, Kansal, Raghav, Kastner, Ryan, Katsavounidis, Erik, Krupa, Jeffrey, Li, Pan, Madireddy, Sandeep, Marx, Ethan, McCormack, Patrick, Meza, Andres, Mitrevski, Jovan, Mohammed, Mohammed Attia, Mokhtar, Farouk, Moreno, Eric, Nagu, Srishti, Narayan, Rohin, Palladino, Noah, Que, Zhiqiang, Park, Sang Eon, Ramamoorthy, Subramanian, Rankin, Dylan, Rothman, Simon, Sharma, Ashish, Summers, Sioni, Vischia, Pietro, Vlimant, Jean-Roch, Weng, Olivia
Publikováno v:
Front. Big Data 5, 787421 (2022)
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing loop to accelerate scientific discovery.
Externí odkaz:
http://arxiv.org/abs/2110.13041
Recently, significant progress has been made in solving sophisticated problems among various domains by using reinforcement learning (RL), which allows machines or agents to learn from interactions with environments rather than explicit supervision.
Externí odkaz:
http://arxiv.org/abs/2001.06930
Akademický článek
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The first contribution of this paper is the development of extremely dense, energy-efficient mixed-signal vector-by-matrix-multiplication (VMM) circuits based on the existing 3D-NAND flash memory blocks, without any need for their modification. Such
Externí odkaz:
http://arxiv.org/abs/1908.02472
The superior density of passive analog-grade memristive crossbars may enable storing large synaptic weight matrices directly on specialized neuromorphic chips, thus avoiding costly off-chip communication. To ensure efficient use of such crossbars in
Externí odkaz:
http://arxiv.org/abs/1906.12045