Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Weng, Olivia"'
Autor:
Baldi, Tommaso, Campos, Javier, Hawks, Ben, Ngadiuba, Jennifer, Tran, Nhan, Diaz, Daniel, Duarte, Javier, Kastner, Ryan, Meza, Andres, Quinnan, Melissa, Weng, Olivia, Geniesse, Caleb, Gholami, Amir, Mahoney, Michael W., Loncar, Vladimir, Harris, Philip, Agar, Joshua, Qin, Shuyu
Extreme data rate scientific experiments create massive amounts of data that require efficient ML edge processing. This leads to unique validation challenges for VLSI implementations of ML algorithms: enabling bit-accurate functional simulations for
Externí odkaz:
http://arxiv.org/abs/2406.19522
With more scientific fields relying on neural networks (NNs) to process data incoming at extreme throughputs and latencies, it is crucial to develop NNs with all their parameters stored on-chip. In many of these applications, there is not enough time
Externí odkaz:
http://arxiv.org/abs/2403.08980
Autor:
Drewes, Colin, Weng, Olivia, Meza, Andres, Althoff, Alric, Kohlbrenner, David, Kastner, Ryan, Richmond, Dustin
Cloud FPGAs strike an alluring balance between computational efficiency, energy efficiency, and cost. It is the flexibility of the FPGA architecture that enables these benefits, but that very same flexibility that exposes new security vulnerabilities
Externí odkaz:
http://arxiv.org/abs/2303.17881
Autor:
Weng, Olivia, Marcano, Gabriel, Loncar, Vladimir, Khodamoradi, Alireza, Sheybani, Nojan, Meza, Andres, Koushanfar, Farinaz, Denolf, Kristof, Duarte, Javier Mauricio, Kastner, Ryan
Deep neural networks use skip connections to improve training convergence. However, these skip connections are costly in hardware, requiring extra buffers and increasing on- and off-chip memory utilization and bandwidth requirements. In this paper, w
Externí odkaz:
http://arxiv.org/abs/2301.07247
Autor:
Borras, Hendrik, Di Guglielmo, Giuseppe, Duarte, Javier, Ghielmetti, Nicolò, Hawks, Ben, Hauck, Scott, Hsu, Shih-Chieh, Kastner, Ryan, Liang, Jason, Meza, Andres, Muhizi, Jules, Nguyen, Tai, Roy, Rushil, Tran, Nhan, Umuroglu, Yaman, Weng, Olivia, Yokuda, Aidan, Blott, Michaela
We present our development experience and recent results for the MLPerf Tiny Inference Benchmark on field-programmable gate array (FPGA) platforms. We use the open-source hls4ml and FINN workflows, which aim to democratize AI-hardware codesign of opt
Externí odkaz:
http://arxiv.org/abs/2206.11791
Autor:
Weng, Olivia
As neural networks have become more powerful, there has been a rising desire to deploy them in the real world; however, the power and accuracy of neural networks is largely due to their depth and complexity, making them difficult to deploy, especiall
Externí odkaz:
http://arxiv.org/abs/2112.06126
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
Residual networks (ResNets) employ skip connections in their networks -- reusing activations from previous layers -- to improve training convergence, but these skip connections create challenges for hardware implementations of ResNets. The hardware m
Externí odkaz:
http://arxiv.org/abs/2102.01351
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Autor:
O'Connell, Caitlin P., Johnson, Kimberly J., Kinzer, Hannah, Olagoke, Ayokunle, Weng, Olivia, Kreuter, Matthew W.
Publikováno v:
In Preventive Medicine Reports August 2023 34