1 code implementation • 27 Feb 2024 • Tatsuhiro Onodera, Martin M. Stein, Benjamin A. Ash, Mandar M. Sohoni, Melissa Bosch, Ryotatsu Yanagimoto, Marc Jankowski, Timothy P. McKenna, Tianyu Wang, Gennady Shvets, Maxim R. Shcherbakov, Logan G. Wright, Peter L. McMahon
On-chip photonic processors for neural networks have potential benefits in both speed and energy efficiency but have not yet reached the scale at which they can outperform electronic processors.
no code implementations • 20 Oct 2023 • jeremie Laydevant, Logan G. Wright, Tianyu Wang, Peter L. McMahon
Human brains and bodies are not hardware running software: the hardware is the software.
no code implementations • 31 Jul 2023 • Peter L. McMahon
There has been a resurgence of interest in optical computing over the past decade, both in academia and in industry, with much of the excitement centered around special-purpose optical computers for neural-network processing.
no code implementations • 28 Jul 2023 • Shi-Yuan Ma, Tianyu Wang, Jérémie Laydevant, Logan G. Wright, Peter L. McMahon
We experimentally demonstrated MNIST classification with a test accuracy of 98% using an optical neural network with a hidden layer operating in the single-photon regime; the optical energy used to perform the classification corresponds to 0. 008 photons per multiply-accumulate (MAC) operation, which is equivalent to 0. 003 attojoules of optical energy per MAC.
no code implementations • 20 Feb 2023 • Maxwell G. Anderson, Shi-Yuan Ma, Tianyu Wang, Logan G. Wright, Peter L. McMahon
We conclude that with well-engineered, large-scale optical hardware, it may be possible to achieve a $100 \times$ energy-efficiency advantage for running some of the largest current Transformer models, and that if both the models and the optical hardware are scaled to the quadrillion-parameter regime, optical computers could have a $>8, 000\times$ energy-efficiency advantage over state-of-the-art digital-electronic processors that achieve 300 fJ/MAC.
1 code implementation • 27 Jul 2022 • Tianyu Wang, Mandar M. Sohoni, Logan G. Wright, Martin M. Stein, Shi-Yuan Ma, Tatsuhiro Onodera, Maxwell G. Anderson, Peter L. McMahon
In image sensing, a measurement, such as of an object's position, is performed by computational analysis of a digitized image.
no code implementations • 6 Dec 2021 • Alexander Lavin, David Krakauer, Hector Zenil, Justin Gottschlich, Tim Mattson, Johann Brehmer, Anima Anandkumar, Sanjay Choudry, Kamil Rocki, Atılım Güneş Baydin, Carina Prunkl, Brooks Paige, Olexandr Isayev, Erik Peterson, Peter L. McMahon, Jakob Macke, Kyle Cranmer, Jiaxin Zhang, Haruko Wainwright, Adi Hanuka, Manuela Veloso, Samuel Assefa, Stephan Zheng, Avi Pfeffer
We present the "Nine Motifs of Simulation Intelligence", a roadmap for the development and integration of the essential algorithms necessary for a merger of scientific computing, scientific simulation, and artificial intelligence.
1 code implementation • 27 Apr 2021 • Logan G. Wright, Tatsuhiro Onodera, Martin M. Stein, Tianyu Wang, Darren T. Schachter, Zoey Hu, Peter L. McMahon
Deep neural networks have become a pervasive tool in science and engineering.
2 code implementations • 27 Apr 2021 • Tianyu Wang, Shi-Yuan Ma, Logan G. Wright, Tatsuhiro Onodera, Brian Richard, Peter L. McMahon
Here, we experimentally demonstrate an optical neural network achieving 99% accuracy on handwritten-digit classification using ~3. 2 detected photons per weight multiplication and ~90% accuracy using ~0. 64 photons (~$2. 4 \times 10^{-19}$ J of optical energy) per weight multiplication.
no code implementations • 9 Mar 2021 • Edwin Ng, Tatsuhiro Onodera, Satoshi Kako, Peter L. McMahon, Hideo Mabuchi, Yoshihisa Yamamoto
We show that the nonlinear stochastic dynamics of a measurement-feedback-based coherent Ising machine (MFB-CIM) in the presence of quantum noise can be exploited to sample degenerate ground and low-energy spin configurations of the Ising model.
Quantum Physics Optics
1 code implementation • 22 Jun 2020 • Peter Cha, Paul Ginsparg, Felix Wu, Juan Carrasquilla, Peter L. McMahon, Eun-Ah Kim
Here we propose the "Attention-based Quantum Tomography" (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state.