Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Sludds, Alexander"'
The scalability of many programmable photonic circuits is limited by the $2\pi$ tuning range needed for the constituent phase shifters. To address this problem, we introduce the concept of a phase-efficient circuit architecture, where the average pha
Externí odkaz:
http://arxiv.org/abs/2408.09673
Autor:
Ou, Shaoyuan, Xue, Kaiwen, Zhou, Lian, Lee, Chun-ho, Sludds, Alexander, Hamerly, Ryan, Zhang, Ke, Feng, Hanke, Kopparapu, Reshma, Zhong, Eric, Wang, Cheng, Englund, Dirk, Yu, Mengjie, Chen, Zaijun
The escalating data volume and complexity resulting from the rapid expansion of artificial intelligence (AI), internet of things (IoT) and 5G/6G mobile networks is creating an urgent need for energy-efficient, scalable computing hardware. Here we dem
Externí odkaz:
http://arxiv.org/abs/2401.18050
Autor:
Larocque, Hugo, Vitullo, Dashiell L. P., Sludds, Alexander, Sattari, Hamed, Christen, Ian, Choong, Gregory, Prieto, Ivan, Leo, Jacopo, Zarebidaki, Homa, Lohani, Sanjaya, Kirby, Brian T., Soykal, Öney O., Soltani, Moe, Ghadimi, Amir H., Englund, Dirk, Heuck, Mikkel
Publikováno v:
ACS Photonics 2024
Thin-Film Lithium Niobate (TFLN) is an emerging integrated photonic platform showing great promise due to its large second-order nonlinearity at microwave and optical frequencies, cryogenic compatibility, large piezoelectric response, and low optical
Externí odkaz:
http://arxiv.org/abs/2312.16746
Autor:
Sludds, Alexander
Machine learning has become ubiquitous in our daily lives, providing unprecedented improvements in image recognition, autonomous driving and conversational AI. To enable this improvement the size of machine learning models has grown exponentially, re
Externí odkaz:
https://hdl.handle.net/1721.1/151701
Autor:
Bandyopadhyay, Saumil, Sludds, Alexander, Krastanov, Stefan, Hamerly, Ryan, Harris, Nicholas, Bunandar, Darius, Streshinsky, Matthew, Hochberg, Michael, Englund, Dirk
As deep neural networks (DNNs) revolutionize machine learning, energy consumption and throughput are emerging as fundamental limitations of CMOS electronics. This has motivated a search for new hardware architectures optimized for artificial intellig
Externí odkaz:
http://arxiv.org/abs/2208.01623
Autor:
Chen, Zaijun, Sludds, Alexander, Davis, Ronald, Christen, Ian, Bernstein, Liane, Heuser, Tobias, Heermeier, Niels, Lott, James A., Reitzenstein, Stephan, Hamerly, Ryan, Englund, Dirk
Deep neural networks (DNNs) are reshaping the field of information processing. With their exponential growth challenging existing electronic hardware, optical neural networks (ONNs) are emerging to process DNN tasks in the optical domain with high cl
Externí odkaz:
http://arxiv.org/abs/2207.05329
Autor:
Hamerly, Ryan, Sludds, Alexander, Bandyopadhyay, Saumil, Chen, Zaijun, Zhong, Zhizhen, Bernstein, Liane, Englund, Dirk
This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neural network inference by dividing the computational task into two step
Externí odkaz:
http://arxiv.org/abs/2207.01777
Autor:
Bernstein, Liane, Sludds, Alexander, Panuski, Christopher, Trajtenberg-Mills, Sivan, Hamerly, Ryan, Englund, Dirk
As deep neural networks (DNNs) grow to solve increasingly complex problems, they are becoming limited by the latency and power consumption of existing digital processors. For improved speed and energy efficiency, specialized analog optical and electr
Externí odkaz:
http://arxiv.org/abs/2205.09103
Autor:
Sludds, Alexander, Bandyopadhyay, Saumil, Chen, Zaijun, Zhong, Zhizhen, Cochrane, Jared, Bernstein, Liane, Bunandar, Darius, Dixon, P. Ben, Hamilton, Scott A., Streshinsky, Matthew, Novack, Ari, Baehr-Jones, Tom, Hochberg, Michael, Ghobadi, Manya, Hamerly, Ryan, Englund, Dirk
Advances in deep neural networks (DNNs) are transforming science and technology. However, the increasing computational demands of the most powerful DNNs limit deployment on low-power devices, such as smartphones and sensors -- and this trend is accel
Externí odkaz:
http://arxiv.org/abs/2203.05466
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Co
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Co
Externí odkaz:
https://hdl.handle.net/1721.1/123135