Free space intra-datacenter interconnects based on 2D optical beam steering enabled by photonic integrated circuits

Data centers are continuously growing in scale and can contain more than one million servers spreading across thousands of racks; requiring a large-scale switching network to provide broadband and reconfigurable interconnections of low latency. Traditional data center network architectures, through the use of electrical packet switches in a multi-tier topology, has fundamental weaknesses such as oversubscription and cabling complexity. Wireless intra-data center interconnection solutions have been proposed to deal with the cabling problem and can simultaneously address the over-provisioning problem by offering efficient topology re-configurability. In this work we introduce a novel free space optical interconnect solution for intra-data center networks that utilizes 2D optical beam steering for the transmitter, and high bandwidth wide-area photodiode arrays for the receiver. This new breed of free space optical interconnects can be developed on a photonic integrated circuit; offering ns switching at sub-µW consumption. The proposed interconnects together with a networking architecture that is suitable for utilizing those devices could support next generation intra-data center networks, fulfilling the requirements of seamless operation, high connectivity, and agility in terms of the reconfiguration time.Peer ReviewedPostprint (published version

Hybrid Optical Switching for Data Center Networks

Current data centers networks rely on electronic switching and point-to-point interconnects.When considering future data center requirements, these solutions will raise issues in terms of flexibility, scalability, performance, and energy consumption. For this reason several optical switched interconnects, which make use of optical switches and wavelength division multiplexing (WDM), have been recently proposed. However, the solutions proposed so far suffer from low flexibility and are not able to provide service differentiation. In this paper we introduce a novel data center network based on hybrid optical switching (HOS). HOS combines optical circuit, burst, and packet switching on the same network. In this way different data center applications can be mapped to the optical transportmechanism that best suits their traffic characteristics. Furthermore, the proposed HOS network achieves high transmission efficiency and reduced energy consumption by using two parallel optical switches. We consider the architectures of both a traditional data center network and the proposed HOS network and present a combined analytical and simulation approach for their performance and energy consumption evaluation. We demonstrate that the proposed HOS data center network achieves high performance and flexibility while considerably reducing the energy consumption of current solutions

Optical Networks and Interconnects

The rapid evolution of communication technologies such as 5G and beyond, rely on optical networks to support the challenging and ambitious requirements that include both capacity and reliability. This chapter begins by giving an overview of the evolution of optical access networks, focusing on Passive Optical Networks (PONs). The development of the different PON standards and requirements aiming at longer reach, higher client count and delivered bandwidth are presented. PON virtualization is also introduced as the flexibility enabler. Triggered by the increase of bandwidth supported by access and aggregation network segments, core networks have also evolved, as presented in the second part of the chapter. Scaling the physical infrastructure requires high investment and hence, operators are considering alternatives to optimize the use of the existing capacity. This chapter introduces different planning problems such as Routing and Spectrum Assignment problems, placement problems for regenerators and wavelength converters, and how to offer resilience to different failures. An overview of control and management is also provided. Moreover, motivated by the increasing importance of data storage and data processing, this chapter also addresses different aspects of optical data center interconnects. Data centers have become critical infrastructure to operate any service. They are also forced to take advantage of optical technology in order to keep up with the growing capacity demand and power consumption. This chapter gives an overview of different optical data center network architectures as well as some expected directions to improve the resource utilization and increase the network capacity

Integrated GHz silicon photonic interconnect with micrometer-scale modulators and detectors

We report an optical link on silicon using micrometer-scale ring-resonator enhanced silicon modulators and waveguide-integrated germanium photodetectors. We show 3 Gbps operation of the link with 0.5 V modulator voltage swing and 1.0 V detector bias. The total energy consumption for such a link is estimated to be ~120 fJ/bit. Such compact and low power monolithic link is an essential step towards large-scale on-chip optical interconnects for future microprocessors

Large-Scale Optical Neural Networks based on Photoelectric Multiplication

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 ($N \gtrsim 10^6$) networks and can be operated at high (GHz) speeds and very low (sub-aJ) 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 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 back-propagation and training that can be performed in the same hardware. This architecture will enable a new class of ultra-low-energy processors for deep learning.Comment: Text: 10 pages, 5 figures, 1 table. Supplementary: 8 pages, 5, figures, 2 table