Neuromorphic Learning VLSI Systems: a Survey
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Unsung Innovators: Lynn Conway and Carver Mead They Literally Wrote the Book on Chip Design
http://www.computerworld.com/s/article/9046420/Unsung_innovators_Lynn_Conway_and_Carver_Mead Unsung innovators: Lynn Conway and Carver Mead They literally wrote the book on chip design By Gina Smith December 3, 2007 12:00 PM ET Computerworld - There is an analogy Lynn Conway brings up when trying to explain what is now known as the "Mead & Conway Revolution" in chip design history. Before the Web, the Internet had been chugging along for years. But it took the World Wide Web, and its systems and standards, to help the Internet burst into our collective consciousness. "What we had took off in that modern sort of way," says Conway today. Before Lynn Conway and Carver Mead's work on chip design, the field was progressing, albeit slowly. "By the mid-1970s, digital system designers eager to create higher-performance devices were frustrated by having to use off-the-shelf large-scale-integration logic," according to Electronic Design magazine, which inducted Mead and Conway into its Hall of Fame in 2002. The situation at the time stymied designers' efforts "to make chips sufficiently compact or cost- effective to turn their very large-scale visions into timely realities." But then Conway and Mead introduced their very large-scale integration (VLSI) methods for combining tens of thousands of transistor circuits on a single chip. And after Mead and Conway's 1980 textbook Introduction to VLSI Design -- and its subsequent storm through the nation's universities -- engineers outside the big semiconductor companies could pump out bigger and better digital chip designs, and do it faster than ever. Their textbook became the bible of chip designers for decades, selling over 70,000 copies. -
Countering the Bohr Thralldom
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 27 July 2021 Countering the Bohr Thralldom Curtis Lacy University of Washington Ph.D. Physics 1970, now retired ORCID 0000-00034-2506-2171, [email protected] Keywords: superposition of states, observable, superluminal, Copenhagen interpretation of quantum mechanics Abstract The Copenhagen interpretation of quantum mechanics and the notion of superposition of states are examined and found problematical. Introduction In 2013, a short article by Carver Mead1 appeared online,2 in which he deplores the stultifying effect of competition for position and influence by the pioneers and originators of contemporary science, particularly relativity and quantum mechanics. In his words, "A bunch of big egos got in the way", and stalled the revolution which had started in the early twentieth century. Mead recounts the story of Niels Bohr and Werner Heisenberg laughing at Charles Townes, inventor of the maser and laser, when he presented his ideas to them. They dismissed him with the condescending judgment that he didn’t understand how quantum mechanics works. The subsequent development of laser technology constitutes a cogent reminder of the perils inherent in casual dismissal of novel ideas. Mead also expresses concern regarding the subsequent development of physics in the twentieth century. The opening statement in his book Collective Electrodynamics declares “It is my firm belief that the last seven decades of the twentieth century will be characterized in history as the dark ages of theoretical physics”. The present note proposes that the currently accepted picture of quantum mechanics, the so-called Copenhagen interpretation of quantum mechanics (hereinafter CIQM), brings in its wake certain difficulties which call for further consideration, perhaps even a thorough revision. -
Large-Scale Optical Neural Networks Based on Photoelectric Multiplication
PHYSICAL REVIEW X 9, 021032 (2019) Large-Scale Optical Neural Networks Based on Photoelectric Multiplication Ryan Hamerly,* Liane Bernstein, Alexander Sludds, Marin Soljačić, and Dirk Englund Research Laboratory of Electronics, MIT, 50 Vassar Street, Cambridge, Massachusetts 02139, USA (Received 12 November 2018; revised manuscript received 21 February 2019; published 16 May 2019) Recent success in deep neural networks has generated strong interest in hardware accelerators to improve speed and energy consumption. This paper presents a new type of photonic accelerator based on coherent detection that is scalable to large (N ≳ 106) networks and can be operated at high (gigahertz) speeds and very low (subattojoule) energies per multiply and accumulate (MAC), using the massive spatial multiplexing enabled by standard free-space optical components. In contrast to previous approaches, both weights and inputs are optically encoded so that the network can be reprogrammed and trained on the fly. Simulations of the network using models for digit and image classification reveal a “standard quantum limit” for optical neural networks, set by photodetector shot noise. This bound, which can be as low as 50 zJ=MAC, suggests that performance below the thermodynamic (Landauer) limit for digital irreversible computation is theoretically possible in this device. The proposed accelerator can implement both fully connected and convolutional networks. We also present a scheme for backpropagation and training that can be performed in the same hardware. This architecture will enable a new class of ultralow-energy processors for deep learning. DOI: 10.1103/PhysRevX.9.021032 Subject Areas: Interdisciplinary Physics, Optoelectronics, Photonics I. INTRODUCTION ideally compatible with training as well as inference. -
Self-Learning Photonic Signal Processor with an Optical Neural Network Chip
Self-learning photonic signal processor with an optical neural network chip Hailong Zhou1Ϯ, Yuhe Zhao1Ϯ, Xu Wang1, Dingshan Gao1, Jianji Dong1 and Xinliang Zhang1 1Wuhan National Laboratory for Optoelectronics, School of Optical and Electronic Information, Huazhong University of Science Ϯ and Technology, Wuhan 430074, China; These authors contributed equally to this work. e-mail: [email protected]; [email protected]. Photonic signal processing is essential in the optical communication and optical computing. Numerous photonic signal processors have been proposed, but most of them exhibit limited reconfigurability and automaticity. A feature of fully automatic implementation and intelligent response is highly desirable for the multipurpose photonic signal processors. Here, we report and experimentally demonstrate a fully self-learning and reconfigurable photonic signal processor based on an optical neural network chip. The proposed photonic signal processor is capable of performing various functions including multichannel optical switching, optical multiple-input- multiple-output descrambler and tunable optical filter. All the functions are achieved by complete self-learning. Our demonstration suggests great potential for chip-scale fully programmable optical signal processing with artificial intelligence. Photonic signal processing is widespread both in the optical communication and optical computing. Almost all aspects of signal processing functions were developed, such as multichannel optical switching1-7, mode (de)multiplexer8,9, optical filter10-14, polarization management15-17, waveguide crossing18,19 and logic gates20-22. Plentiful photonic signal processing devices have been reported based on either discrete components or photonic integrated circuits1,9,15,20-24. Photonic signal processing devices based on discrete components are usually bulkier and less power efficient, whereas a photonic integrated signal processing chip has a much smaller footprint and a higher power efficiency. -
An Optical Neural Chip for Implementing Complex-Valued
ARTICLE https://doi.org/10.1038/s41467-020-20719-7 OPEN An optical neural chip for implementing complex- valued neural network ✉ ✉ H. Zhang1,M.Gu 2,3 , X. D. Jiang 1 , J. Thompson 3, H. Cai4, S. Paesani5, R. Santagati 5, A. Laing 5, Y. Zhang 1,6, M. H. Yung7,8, Y. Z. Shi 1, F. K. Muhammad1,G.Q.Lo9, X. S. Luo 9, B. Dong9, D. L. Kwong4, ✉ ✉ L. C. Kwek 1,3,10 & A. Q. Liu 1 Complex-valued neural networks have many advantages over their real-valued counterparts. 1234567890():,; Conventional digital electronic computing platforms are incapable of executing truly complex- valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical inter- ference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real- valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart. 1 Quantum Science and Engineering Centre (QSec), Nanyang Technological University, 50 Nanyang Ave, 639798 Singapore, Singapore. -
The Optical Mouse: Early Biomimetic Embedded Vision
The Optical Mouse: Early Biomimetic Embedded Vision Richard F. Lyon Abstract The 1980 Xerox optical mouse invention, and subsequent product, was a successful deployment of embedded vision, as well as of the Mead–Conway VLSI design methodology that we developed at Xerox PARC in the late 1970s. The de- sign incorporated an interpretation of visual lateral inhibition, essentially mimicking biology to achieve a wide dynamic range, or light-level-independent operation. Con- ceived in the context of a research group developing VLSI design methodologies, the optical mouse chip represented an approach to self-timed semi-digital design, with the analog image-sensing nodes connecting directly to otherwise digital logic using a switch-network methodology. Using only a few hundred gates and pass tran- sistors in 5-micron nMOS technology, the optical mouse chip tracked the motion of light dots in its field of view, and reported motion with a pair of 2-bit Gray codes for x and y relative position—just like the mechanical mice of the time. Besides the chip, the only other electronic components in the mouse were the LED illuminators. Fig. 1 The Xerox optical mouse chip in its injection- molded dual-inline package (DIP) of clear plastic, with pins stuck into a conductive packaging foam. The bond wires connecting the chip’s pads to the lead frame are (barely) visible. Richard F. Lyon Google Inc., Mountain View CA, e-mail: [email protected] 1 2 Richard F. Lyon Fig. 2 The Winter 1982 Xe- rox World internal magazine cover featuring the Electron- ics Division and their 3-button mechanical and optical mouse developments, among other electronic developments. -
Class-Specific Differential Detection in Diffractive Optical Neural Networks
Class-specific Differential Detection in Diffractive Optical Neural Networks Improves Inference Accuracy Jingxi Li 1,2,3†, Deniz Mengu 1,2,3†, Yi Luo 1,2,3 , Yair Rivenson 1,2,3 , Aydogan Ozcan 1,2,3* 1Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA 2Bioengineering Department, University of California, Los Angeles, CA, 90095, USA 3California NanoSystems Institute, University of California, Los Angeles, CA, 90095, USA †Equal contributing authors. *Corresponding author: [email protected] Abstract Optical computing provides unique opportunities in terms of parallelization, scalability, power efficiency and computational speed, and has attracted major interest for machine learning. Diffractive deep neural networks have been introduced earlier as an optical machine learning framework that uses task-specific diffractive surfaces designed by deep learning to all-optically perform inference, achieving promising performance for object classification and imaging. Here we demonstrate systematic improvements in diffractive optical neural networks based on a differential measurement technique that mitigates the strict non-negativity constraint of light intensity. In this differential detection scheme, each class is assigned to a separate pair of detectors, behind a diffractive optical network, and the class inference is made by maximizing the normalized signal difference between the photodetector pairs. Using this differential detection scheme, involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 Million neurons, we numerically achieved blind testing accuracies of 98.54%, 90.54% and 48.51% for MNIST, Fashion-MNIST and grayscale CIFAR-10 datasets, respectively. Moreover, by utilizing the inherent parallelization capability of optical systems, we reduced the cross-talk and optical signal coupling between the positive and negative detectors of each class by dividing the optical path into two jointly-trained diffractive neural networks that work in parallel. -
Fiber Echo State Network Analogue for High-Bandwidth Dual-Quadrature Signal Processing
Vol. 27, No. 3 | 4 Feb 2019 | OPTICS EXPRESS 2387 Fiber echo state network analogue for high-bandwidth dual-quadrature signal processing MARIIA SOROKINA,SERGEY SERGEYEV, AND SERGEI TURITSYN Aston University, B4 7ET, United Kingdom *[email protected] Abstract: All-optical platforms for recurrent neural networks can offer higher computational speed and energy efficiency. To produce a major advance in comparison with currently available digital signal processing methods, the new system would need to have high bandwidth and operate both signal quadratures (power and phase). Here we propose a fiber echo state network analogue (FESNA) — the first optical technology that provides both high (beyond previous limits) bandwidth and dual-quadrature signal processing. We demonstrate applicability of the designed system for prediction tasks and for the mitigation of distortions in optical communication systems with multilevel dual-quadrature encoded signals. © 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement 1. Introduction Machine learning, in particular neural networks (NNs), have recently attracted renewed and fast- growing interest due to novel designs, advanced hardware implementations, and new applications. The surge of interest in recurrent neural networks (RNNs) is due to the broad spectrum of successful applications, which ranges from image analysis and classification, speech recognition, and language translation to more specific tasks like solving inverse imaging problems or signal processing in wireless and optical communications. In particular, in optical communications, RNNs have been used to compensate for nonlinear signal distortions [1–3] and nonlinearity mitigation for QPSK and 16-QAM modulation formats [4–6]. However, many applications are limited because the typical scale of RNNs requires a significant amount of time for training and processing, which represents a challenging computational burden. -
Carver A. Mead Papers, 1956-2016, Bulk 1968-1994
http://oac.cdlib.org/findaid/ark:/13030/c8k64qc0 No online items Finding Aid for the Carver A. Mead Papers, 1956-2016, bulk 1968-1994 Penelope Neder-Muro & Mariella Soprano California Institute of Technology Archives and Special Collections December 2017 (Updated December 2018) 1200 East California Blvd. MC B215-74 Pasadena, California 91125 [email protected] URL: http://archives.caltech.edu/ Finding Aid for the Carver A. 10284-MS 1 Mead Papers, 1956-2016, bulk 1968-1994 Language of Material: English Contributing Institution: California Institute of Technology Archives and Special Collections Title: Carver A. Mead Papers creator: Mead, Carver A. Identifier/Call Number: 10284-MS Physical Description: 49 linear feet(85 archives boxes, 2 half size boxes, 4 oversize flat boxes, 2 record cartons, 7 tubes, 1 postcard box, and digital files) Date (inclusive): 1956-2017 Date (bulk): 1968-1994 Abstract: Carver A. Mead, Caltech Alumnus (B.S. 1956, M.S. 1957, PhD 1960) and the Gordon and Betty Moore Professor of Engineering and Applied Science, Emeritus, taught at Caltech for over forty years. Mead retired from teaching in 1999, but remains active in his research and in the Caltech community. Mead's papers consist of correspondence, biographical material, teaching notes, notebooks, book drafts, audiovisual material, artifacts, and research material, including his work with very large scale integration (VLSI) of integrated circuits. Conditions Governing Access The collection is open for research. Researchers must apply in writing for access. Some files are confidential and will remain closed for an indefinite period. Researchers may request information about closed files from the Caltech Archives. -
Carver Mead | Research Overview
Carver Mead | Research Overview http://www.carvermead.caltech.edu/research.html Carver Mead Gordon & Betty Moore Professor of Engineering & Applied Science, Emeritus Contact: Donna L. Fox Email: [email protected] Phone: 626.395.2812 Home Research Overview Research Overview Honors and Awards Power System History Publications Patents Teaching Talks YouTube Video Channel Articles and Interviews Caltech History Curriculum Vitae Carver Mead New Adventures Fund For the past 50 years, Carver Mead has dedicated his research, teaching, and public presentation to the physics and technology of electron devices. This effort has been divided among basic physics, practical devices, and seeing the solid state as a medium for the realization of novel and enormously concurrent computing structures. Listed below are a number of important contributions that were made over that period. 1960 Transistor switching analysis. Dissertation (Ph.D.), California Institute of Technology (0). Proposed and demonstrated the first three-terminal solid-state device operating with electron tunneling and hot-electron transport as its operating principles (1). 1961 Initiated a systematic investigation of the energy-momentum relation of electrons in the energy gap of insulators (2, 3, 4) and semiconductors (5, 6, 7). These studies involved exquisite control of nanometer-scale device dimensions, albeit in only one dimension. 1962 First demonstration that hot electrons in Gold retained their energy for distances in the nanometer range (8). 1 of 9 7/6/2020, 8:44 AM Carver Mead | Research Overview http://www.carvermead.caltech.edu/research.html 1963 With W. Spitzer (9, 10, 11), established the systematic role of interface states in determining the energy at interfaces of III-V compounds, independent of the detailed nature of the interface. -
2. Quantum Patents
6.2.21_HANEY_FINAL (DO NOT DELETE) 6/2/2021 7:44 PM ARTICLE QUANTUM PATENTS BRIAN HANEY1 ABSTRACT Quantum Patents are patents with claims relating to quantum computing. The number of Quantum Patents granted by the USPTO is rapidly increasing each year. In addition, Quantum Patents are increasing in total market value. While the literature on technology patents is visibly scaling, literature specifically fo- cused on Quantum Patents is non-existent. This Article draws on a growing body of quantum computing, intellectual property, and technology law scholarship to provide novel Quantum Patent analysis and critique. This Article contributes the first empirical Quantum Patent review, including novel technology descriptions, market modeling, and legal analysis relating to Quantum Patent claims. First, this Article discusses the two main technical ap- proaches to Quantum Computing. This discussion explores the relationship be- tween Adiabatic Quantum Computers and Gate-Model Quantum Computers, as well as various quantum software frameworks. Second, this Article models an evolving Quantum Patent dataset, offering economic insights, claims analysis, and patent valuation strategies. The data models provide insight into an un- charted patent market alcove, shining light on a completely new economy. ABSTRACT......................................................................................................... 64 I. INTRODUCTION .............................................................................................. 65 II. QUANTUM COMPUTERS -
Carver Mead Oral History
Carver Mead Oral History Interviewed by: Doug Fairbairn Recorded: May 27, 2009 Mountain View, California CHM Reference number: X4309.2008 © 2009 Computer History Museum Oral History of Carver Mead Doug Fairbairn: I'm Douglas Fairbairn. Today I'm interviewing Dr. Carver Mead, the Gordon and Betty Moore Professor of Engineering and Applied Science Emeritus at California Institute of Technology or Caltech in Pasadena, California. I've actually known Carver for over 30 years. We originally met in connection with his work in VLSI design and design methodology, and we're using this opportunity to look at some of the roots of that, some of Carver's background and in particular the roots of his work in VLSI design and VLSI design methodology and how that important work came to such an important point. In order to set a context for that, we're going to delve in to Carver's early years and growing up in southern California and so forth, but we will not go in to nearly the detail that some others have. I want to point out to the viewers of this tape that there has been an extensive interview of Carver's background and career in most of the important areas that was done by the Chemical Heritage Foundation in 2004 and 2005, and the transcripts of that very extensive set of interviews that spanned three days are available from them. So we are going to focus in this particular interview on a summary of his background and how it eventually led to his work in VLSI design and VLSI design methodologies and something about the impact that has had subsequently.