Paul R. Prucnal

Bio: Paul R. Prucnal is an academic researcher from Princeton University. The author has contributed to research in topics: Photonics & Optical switch. The author has an hindex of 53, co-authored 607 publications receiving 12740 citations. Previous affiliations of Paul R. Prucnal include The Chinese University of Hong Kong & Tsinghua University.

Spread spectrum fiber-optic local area network using optical processing

Abstract: Spread spectrum code division multiple access (CDMA) allows asynchronous multiple access to a local area network (LAN) with no waiting. The additional bandwidth required by spread spectrum can be accommodated by using a fiber-optic channel and incoherent optical signal processing. New CDMA sequences are designed specifically for optical processing. It is shown that increasing the number of chips per bit, by using optical processing, allows an increase in capacity of a CDMA LAN. An experiment is performed demonstrating the performance of an optical CDMA LAN, operating at 100 Mbd with three users.

A terahertz optical asymmetric demultiplexer (TOAD)

Abstract: A device capable of demultiplexing Tb/s pulse trains that requires less than 1 pJ of switching energy and can be integrated on a chip is presented. The device consists of an optical nonlinear element asymmetrically placed in a short fiber loop. Its switching time is determined by the off-center position of the nonlinear element within the loop, and therefore it can use the strong, slow optical nonlinearities found in semiconductors, which all other fast demultiplexers seek to avoid. The switch's operation at 50 Gb/s is demonstrated, using 600-fJ control pulses. >

Neuromorphic photonic networks using silicon photonic weight banks.

Abstract: Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using “neural compiler” to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.

Photonics for artificial intelligence and neuromorphic computing

Abstract: Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges. Photonics offers an attractive platform for implementing neuromorphic computing due to its low latency, multiplexing capabilities and integrated on-chip technology.

Photonics for artificial intelligence and neuromorphic computing

Abstract: Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence, in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, in particular, related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

[서평]「Applied Cryptography」

Abstract: The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

Optical burst switching (OBS) - a new paradigm for an optical Internet

Abstract: To support bursty traffic on the Internet (and especially WWW) efficiently, optical burst switching (OBS) is proposed as a way to streamline both protocols and hardware in building the future generation Optical Internet. By leveraging the attractive properties of optical communications and at the same time, taking into account its limitations, OBS combines the best of optical circuit-switching and packet/cell switching. In this paper, the general concept of OBS protocols and in particular, those based on Just-Enough-Time (JET), is described, along with the applicability of OBS protocols to IP over WDM. Specific issues such as the use of fiber delay-lines (FDLs) for accommodating processing delay and/or resolving conflicts are also discussed. In addition, the performance of JET-based OBS protocols which use an offset time along with delayed reservation to achieve efficient utilization of both bandwidth and FDLs as well as to support priority-based routing is evaluated.

Deep learning with coherent nanophotonic circuits

Abstract: Artificial Neural Networks have dramatically improved performance for many machine learning tasks. We demonstrate a new architecture for a fully optical neural network that enables a computational speed enhancement of at least two orders of magnitude and three orders of magnitude in power efficiency over state-of-the-art electronics.