7,277 research outputs found

    Principles of Neuromorphic Photonics

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    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 artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure

    Gossip-based service monitoring platform for wireless edge cloud computing

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    Edge cloud computing proposes to support shared services, by using the infrastructure at the network's edge. An important problem is the monitoring and management of services across the edge environment. Therefore, dissemination and gathering of data is not straightforward, differing from the classic cloud infrastructure. In this paper, we consider the environment of community networks for edge cloud computing, in which the monitoring of cloud services is required. We propose a monitoring platform to collect near real-time data about the services offered in the community network using a gossip-enabled network. We analyze and apply this gossip-enabled network to perform service discovery and information sharing, enabling data dissemination among the community. We implemented our solution as a prototype and used it for collecting service monitoring data from the real operational community network cloud, as a feasible deployment of our solution. By means of emulation and simulation we analyze in different scenarios, the behavior of the gossip overlay solution, and obtain average results regarding information propagation and consistency needs, i.e. in high latency situations, data convergence occurs within minutes.Peer ReviewedPostprint (author's final draft

    Interconnection Networks for Scalable Quantum Computers

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    We show that the problem of communication in a quantum computer reduces to constructing reliable quantum channels by distributing high-fidelity EPR pairs. We develop analytical models of the latency, bandwidth, error rate and resource utilization of such channels, and show that 100s of qubits must be distributed to accommodate a single data communication. Next, we show that a grid of teleportation nodes forms a good substrate on which to distribute EPR pairs. We also explore the control requirements for such a network. Finally, we propose a specific routing architecture and simulate the communication patterns of the Quantum Fourier Transform to demonstrate the impact of resource contention.Comment: To appear in International Symposium on Computer Architecture 2006 (ISCA 2006

    Efficient Cache Coherence on Manycore Optical Networks

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    Ever since industry has turned to parallelism instead of frequency scaling to improve processor performance, multicore processors have continued to scale to larger and larger numbers of cores. Some believe that multicores will have 1000 cores or more by the middle of the next decade. However, their promise of increased performance will only be reached if their inherent scaling challenges are overcome. One such major scaling challenge is the viability of efficient cache coherence with large numbers of cores. Meanwhile, recent advances in nanophotonic device manufacturing are making CMOS-integrated optics a realityâ interconnect technology which can provide significantly more bandwidth at lower power than conventional electrical analogs. The contributions of this paper are two-fold. (1) It presents ATAC, a new manycore architecture that augments an electrical mesh network with an optical network that performs highly efficient broadcasts. (2) It introduces ACKwise, a novel directory-based cache coherence protocol that provides high performance and scalability on any large-scale manycore interconnection net- work with broadcast capability. Performance evaluation studies using analytical models show that (i) a 1024-core ATAC chip using ACKwise achieves a speedup of 3.9Ã compared to a similarly-sized pure electrical mesh manycore with a conventional limited directory protocol; (ii) the ATAC chip with ACKwise achieves a speedup of 1.35Ã compared to the electrical mesh chip with ACKwise; and (iii) a pure electrical mesh chip with ACKwise achieves a speedup of 2.9Ã over the same chip using a conventional limited directory protocol

    Energy Scalability of On-Chip Interconnection Networks in Multicore Architectures

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    On-chip interconnection networks (OCNs) such as point-to-point networks and buses form the communication backbone in systems-on-a-chip, multicore processors, and tiled processors. OCNs can consume significant portions of a chip's energy budget, so analyzing their energy consumption early in the design cycle becomes important for architectural design decisions. Although numerous studies have examined OCN implementation and performance, few have examined energy. This paper develops an analytical framework for energy estimation in OCNs and presents results based on both analytical models of communication patterns and real network traces from applications running on a tiled multicore processor. Our analytical framework supports arbitrary OCN topologies under arbitrary communication patterns while accounting for wire length, switch energy, and network contention. It is the first to incorporate the effects of communication locality and network contention, and use real traces extensively. This paper compares the energy of point-to-point networks against buses under varying degrees of communication locality. The results indicate that, for 16 or more processors, a one-dimensional and a two-dimensional point-to-point network provide 66% and 82% energy savings, respectively, over a bus assuming that processors communicate with equal likelihood. The energy savings increase for patterns which exhibit locality. For the two-dimensional point-to-point OCN of the Raw tiled microprocessor, contention contributes a maximum of just 23% of the OCN energy, using estimated values for channel, switch control logic, and switch queue buffer energy of 34.5pJ, 17pJ, and 12pJ, respectively. Our results show that the energy-delay product per message decreases with increasing processor message injection rate
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