Architectural Exploration and Design Methodologies of Photonic Interconnection Networks

Photonic technology is becoming an increasingly attractive solution to the problems facing today's electronic chip-scale interconnection networks. Recent progress in silicon photonics research has enabled the demonstration of all the necessary optical building blocks for creating extremely high-bandwidth density and energy-efficient links for on- and off-chip communications. From the feasibility and architecture perspective however, photonics represents a dramatic paradigm shift from traditional electronic network designs due to fundamental differences in how electronics and photonics function and behave. As a result of these differences, new modeling and analysis methods must be employed in order to properly realize a functional photonic chip-scale interconnect design. In this work, we present a methodology for characterizing and modeling fundamental photonic building blocks which can subsequently be combined to form full photonic network architectures. We also describe a set of tools which can be utilized to assess the physical-layer and system-level performance properties of a photonic network. The models and tools are integrated in a novel open-source design and simulation environment called PhoenixSim. Next, we leverage PhoenixSim for the study of chip-scale photonic networks. We examine several photonic networks through the synergistic study of both physical-layer metrics and system-level metrics. This holistic analysis method enables us to provide deeper insight into architecture scalability since it considers insertion loss, crosstalk, and power dissipation. In addition to these novel physical-layer metrics, traditional system-level metrics of bandwidth and latency are also obtained. Lastly, we propose a novel routing architecture known as wavelength-selective spatial routing. This routing architecture is analogous to electronic virtual channels since it enables the transmission of multiple logical optical channels through a single physical plane (i.e. the waveguides). The available wavelength channels are partitioned into separate groups, and each group is routed independently in the network. Each partition is spectrally multiplexed, as opposed to temporally multiplexed in the electronic case. The wavelength-selective spatial routing technique benefits network designers by provider lower contention and increased path diversity

An Energy and Performance Exploration of Network-on-Chip Architectures

In this paper, we explore the designs of a circuit-switched router, a wormhole router, a quality-of-service (QoS) supporting virtual channel router and a speculative virtual channel router and accurately evaluate the energy-performance tradeoffs they offer. Power results from the designs placed and routed in a 90-nm CMOS process show that all the architectures dissipate significant idle state power. The additional energy required to route a packet through the router is then shown to be dominated by the data path. This leads to the key result that, if this trend continues, the use of more elaborate control can be justified and will not be immediately limited by the energy budget. A performance analysis also shows that dynamic resource allocation leads to the lowest network latencies, while static allocation may be used to meet QoS goals. Combining the power and performance figures then allows an energy-latency product to be calculated to judge the efficiency of each of the networks. The speculative virtual channel router was shown to have a very similar efficiency to the wormhole router, while providing a better performance, supporting its use for general purpose designs. Finally, area metrics are also presented to allow a comparison of implementation costs

A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)

Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multi-core neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure

Modeling and Analysis of the Performance of Exascale Photonic Networks

"This is the peer reviewed version of the following article: Duro, José, Jose A. Pascual, Salvador Petit, Julio Sahuquillo, and María E. Gómez. 2018. Modeling and Analysis of the Performance of Exascale Photonic Networks. Concurrency and Computation: Practice and Experience 31 (21). Wiley. doi:10.1002/cpe.4773, which has been published in final form at https://doi.org/10.1002/cpe.4773. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] Photonics technology has become a promising and viable alternative for both on-chip and off-chip interconnection networks of future Exascale systems. Nevertheless, this technology is not mature enough yet in this context, so research efforts focusing on photonic networks are still required to achieve realistic suitable network implementations. In this regard, system-level photonic network simulators can help guide designers to assess the multiple design choices. Most current research is done on electrical network simulators, whose components work widely different from photonics components. In this work, we summarize and compare the working behavior of both technologies which includes the use of optical routers, wavelength-division multiplexing and circuit switching among others. After implementing them into a well-known simulation framework, an extensive simulation study has been carried out using realistic photonic network configurations with synthetic and realistic traffic. Experimental results show that, compared to electrical networks, optical networks can reduce the execution time of the studied real workloads in almost one order of magnitude. Our study also reveals that the photonic configuration highly impacts on the network performance, being the bandwidth per channel and the message length the most important parameters.This work was supported by the ExaNeSt project, funded by the European Union's Horizon 2020 Research and Innovation Program under grant 671553, and by the Spanish Ministerio de Economía y Competitividad (MINECO) and Plan E funds under grant TIN2015-66972-C5-1-R. Pascual was supported by a HiPEAC Collaboration Grant.Duro-Gómez, J.; Pascual Pérez, JA.; Petit Martí, SV.; Sahuquillo Borrás, J.; Gómez Requena, ME. (2019). Modeling and Analysis of the Performance of Exascale Photonic Networks. Concurrency and Computation Practice and Experience. 31(21):1-12. https://doi.org/10.1002/cpe.4773S1123121Top500 website. Accessed January2018.Kodi, A. K., Neel, B., & Brantley, W. C. (2014). Photonic Interconnects for Exascale and Datacenter Architectures. 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Hyperdrive: A Multi-Chip Systolically Scalable Binary-Weight CNN Inference Engine

Deep neural networks have achieved impressive results in computer vision and machine learning. Unfortunately, state-of-the-art networks are extremely compute and memory intensive which makes them unsuitable for mW-devices such as IoT end-nodes. Aggressive quantization of these networks dramatically reduces the computation and memory footprint. Binary-weight neural networks (BWNs) follow this trend, pushing weight quantization to the limit. Hardware accelerators for BWNs presented up to now have focused on core efficiency, disregarding I/O bandwidth and system-level efficiency that are crucial for deployment of accelerators in ultra-low power devices. We present Hyperdrive: a BWN accelerator dramatically reducing the I/O bandwidth exploiting a novel binary-weight streaming approach, which can be used for arbitrarily sized convolutional neural network architecture and input resolution by exploiting the natural scalability of the compute units both at chip-level and system-level by arranging Hyperdrive chips systolically in a 2D mesh while processing the entire feature map together in parallel. Hyperdrive achieves 4.3 TOp/s/W system-level efficiency (i.e., including I/Os)---3.1x higher than state-of-the-art BWN accelerators, even if its core uses resource-intensive FP16 arithmetic for increased robustness

An Experimental Study of Reduced-Voltage Operation in Modern FPGAs for Neural Network Acceleration

We empirically evaluate an undervolting technique, i.e., underscaling the circuit supply voltage below the nominal level, to improve the power-efficiency of Convolutional Neural Network (CNN) accelerators mapped to Field Programmable Gate Arrays (FPGAs). Undervolting below a safe voltage level can lead to timing faults due to excessive circuit latency increase. We evaluate the reliability-power trade-off for such accelerators. Specifically, we experimentally study the reduced-voltage operation of multiple components of real FPGAs, characterize the corresponding reliability behavior of CNN accelerators, propose techniques to minimize the drawbacks of reduced-voltage operation, and combine undervolting with architectural CNN optimization techniques, i.e., quantization and pruning. We investigate the effect of environmental temperature on the reliability-power trade-off of such accelerators. We perform experiments on three identical samples of modern Xilinx ZCU102 FPGA platforms with five state-of-the-art image classification CNN benchmarks. This approach allows us to study the effects of our undervolting technique for both software and hardware variability. We achieve more than 3X power-efficiency (GOPs/W) gain via undervolting. 2.6X of this gain is the result of eliminating the voltage guardband region, i.e., the safe voltage region below the nominal level that is set by FPGA vendor to ensure correct functionality in worst-case environmental and circuit conditions. 43% of the power-efficiency gain is due to further undervolting below the guardband, which comes at the cost of accuracy loss in the CNN accelerator. We evaluate an effective frequency underscaling technique that prevents this accuracy loss, and find that it reduces the power-efficiency gain from 43% to 25%.Comment: To appear at the DSN 2020 conferenc