Zobrazeno 1 - 10
of 106
pro vyhledávání: '"Nahmias Mitchell A."'
Autor:
Ferreira de Lima Thomas, Tait Alexander N., Mehrabian Armin, Nahmias Mitchell A., Huang Chaoran, Peng Hsuan-Tung, Marquez Bicky A., Miscuglio Mario, El-Ghazawi Tarek, Sorger Volker J., Shastri Bhavin J., Prucnal Paul R.
Publikováno v:
Nanophotonics, Vol 9, Iss 13, Pp 4055-4073 (2020)
Microelectronic computers have encountered challenges in meeting all of today’s demands for information processing. Meeting these demands will require the development of unconventional computers employing alternative processing models and new devic
Externí odkaz:
https://doaj.org/article/3b6f4416228740bd8d098767b133457f
Autor:
Ferreira de Lima Thomas, Shastri Bhavin J., Tait Alexander N., Nahmias Mitchell A., Prucnal Paul R.
Publikováno v:
Nanophotonics, Vol 6, Iss 3, Pp 577-599 (2017)
As society’s appetite for information continues to grow, so does our need to process this information with increasing speed and versatility. Many believe that the one-size-fits-all solution of digital electronics is becoming a limiting factor in ce
Externí odkaz:
https://doaj.org/article/a15c872daa364120bf13c3212c1a1ec0
Analog electronic and optical computing exhibit tremendous advantages over digital computing for accelerating deep learning when operations are executed at low precision. In this work, we derive a relationship between analog precision, which is limit
Externí odkaz:
http://arxiv.org/abs/2102.06365
Many neural network quantization techniques have been developed to decrease the computational and memory footprint of deep learning. However, these methods are evaluated subject to confounding tradeoffs that may affect inference acceleration or resou
Externí odkaz:
http://arxiv.org/abs/2102.06366
Autor:
Nahmias, Mitchell A., Peng, Hsuan-Tung, de Lima, Thomas Ferreira, Huang, Chaoran, Tait, Alexander N., Shastri, Bhavin J., Prucnal, Paul R.
There has been a recent surge of interest in the implementation of linear operations such as matrix multipications using photonic integrated circuit technology. However, these approaches require an efficient and flexible way to perform nonlinear oper
Externí odkaz:
http://arxiv.org/abs/2012.08516
Autor:
de Lima, Thomas Ferreira, Tait, Alexander N., Saeidi, Hooman, Nahmias, Mitchell A., Peng, Hsuan-Tung, Abbaslou, Siamak, Shastri, Bhavin J., Prucnal, Paul R.
Neuromorphic photonics relies on efficiently emulating analog neural networks at high speeds. Prior work showed that transducing signals from the optical to the electrical domain and back with transimpedance gain was an efficient approach to implemen
Externí odkaz:
http://arxiv.org/abs/1907.07325
Autor:
Bangari, Viraj, Marquez, Bicky A., Miller, Heidi B., Tait, Alexander N., Nahmias, Mitchell A., de Lima, Thomas Ferreira, Peng, Hsuan-Tung, Prucnal, Paul R., Shastri, Bhavin J.
Convolutional Neural Networks (CNNs) are powerful and highly ubiquitous tools for extracting features from large datasets for applications such as computer vision and natural language processing. However, a convolution is a computationally expensive
Externí odkaz:
http://arxiv.org/abs/1907.01525
Autor:
Tait, Alexander N., Ma, Philip Y., de Lima, Thomas Ferreira, Blow, Eric C., Chang, Matthew P., Nahmias, Mitchell A., Shastri, Bhavin J., Prucnal, Paul R.
Multi-antenna radio front-ends generate a multi-dimensional flood of information, most of which is partially redundant. Redundancy is eliminated by dimensionality reduction, but contemporary digital processing techniques face harsh fundamental tradeo
Externí odkaz:
http://arxiv.org/abs/1903.03474
Autor:
Tait, Alexander N., de Lima, Thomas Ferreira, Nahmias, Mitchell A., Miller, Heidi B., Peng, Hsuan-Tung, Shastri, Bhavin J., Prucnal, Paul R.
Publikováno v:
Phys. Rev. Applied 11, 064043 (2019)
There has been a recently renewed interest in neuromorphic photonics, a field promising to access pivotal and unexplored regimes of machine intelligence. Progress has been made on isolated neurons and analog interconnects; nevertheless, this renewal
Externí odkaz:
http://arxiv.org/abs/1812.11898
Autor:
Shastri, Bhavin J., Tait, Alexander N., de Lima, Thomas Ferreira, Nahmias, Mitchell A., Peng, Hsuan-Tung, Prucnal, Paul R.
In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in ar
Externí odkaz:
http://arxiv.org/abs/1801.00016