Advances in CMOS-compatible photonic elements have made it plausible to exploit nanophotonic communications to overcome the limitations of traditional NoCs. Amongst various proposed nanophotonic architectures, optical crossbars have been shown to provide high performance in terms of bandwidth and latency. In general, optical crossbars provide a vast volume of network resources that are shared among all the cores within the chip. In this paper, we present a fair and efficient admission control mechanism for shared wavelengths and buffer space in optical crossbars. We model buffer management and wavelength assignment as a utility-based convex optimization problem, whose solution determines the admission control policy. Thanks to efficient convex optimization techniques, we obtain the globally optimal solution of the admission control optimization problem by using simple and yet efficient iterative algorithms. We cast our solution procedure as an iterative algorithm to be implemented a central admission controller. Our experimental results corroborate the gain that can be obtained by using such an admission controller to manage the shared resources of the system. Furthermore, they confirm that the proposed admission control algorithm works well for various traffic patterns and parameters, and evinces a tractable scalability with increase in the number of cores of the crossbar.
The architecture proposed by K @cite_25 is an instance of an optical crossbar where several Single Write, Multiple Read (SWMR) busses provide full connection among nodes. Each SWMR bus is dedicated exclusively to one node for sending data to others, whereas all other nodes can read data from all busses. A comparison study of worst-case optical losses for various crossbar implementations is presented in @cite_53 . @cite_21 introduce , which is a hybrid crossbar with nodes partitioned into clusters. While intra-cluster communication is done using smaller electrical crossbars, inter-cluster communication is realized by a SWMR optical crossbar. Unlike the architecture proposed in @cite_25 , optical packets are not broadcast to all nodes, but rather, the intended receiver is selected by auxiliary prior to each communication.
@cite_53 @cite_21 @cite_25
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The many cores design research community have shown high interest in optical crossbars on chip for more than a decade. Key properties of optical crossbars, namely a) contention free data routing b) low latency communication and c) potential for high bandwidth through the use of WDM, motivate several implementations of this type of interconnect. These implementations demonstrate very different scalability and power efficiency ability depending on three key design factors: a) the network topology, b) the considered layout and the c) the injection losses induced by the fabrication process. In this paper, the worst-case optical losses of crossbar implementations are compared according to the factors mentioned above. The comparison results has the potential to help many cores system designer to select the most appropriate crossbar implementation according, for instance, to the number of IP cores and the chip die size. In implementations of neuromorphic computing systems (NCS), memristor and its crossbar topology have been widely used to realize fully connected neural networks. However, many neural networks utilized in real applications often have a sparse connectivity, which is hard to be efficiently mapped to a crossbar structure. Moreover, the scale of the neural networks is normally much larger than that can be offered by the latest integration technology of memristor crossbars. In this work, we propose AutoNCS -- an EDA framework that can automate the NCS designs that combine memristor crossbars and discrete synapse modules. The connections of the neural networks are clustered to improve the utilization of the memristor elements in crossbar structures by taking into account the physical design cost of the NCS. Our results show that AutoNCS can substantially enhance the utilization efficiency of memristor crossbars while reducing the wirelength, area and delay of the physical designs of the NCS. Input-Output buffered crossbars are popular building blocks for scalable high-speed switching because they require minimum speed-up of memory bandwidth. Scaling the design of crossbar switches to large capacities is limited by technology issues such as the reconfiguration of high-speed fabrics or power consumption. In addition, these crossbar architechtures typically schedule and transfer in terms of fixed size envelopes. Thus when they are used in the context of IP networks where packets are of variable size, the incoming packets need to be fragmented into fixed size envelopes. This fragmentation can lead to, possibly large [1], loss of bandwidth and even instability. This paper proposes a new method for switching variable sized packets over a crossbar switch that i) allows maximum utilization of switch bandwidth and avoids the fragmentation effect, and ii) allows designers to use much larger envelopes for transfering data over the fabric and thus minimizes the reconfiguration frequency of the fabric. Reducing the scheduling frequency makes implementation of complex schedulers practical and enables us to build ultra-fast switches incorporating optical technology that can provide bandwidth and delay guarantees. Synapse crossbar is an elementary structure in neuromorphic computing systems (NCS). However, the limited size of crossbars and heavy routing congestion impede the NCS implementation of large neural networks. In this paper, we propose a two-step framework (namely, group scissor) to scale NCS designs to large neural networks. The first step rank clipping integrates low-rank approximation into the training to reduce total crossbar area. The second step is group connection deletion, which structurally prunes connections to reduce routing congestion between crossbars. Tested on convolutional neural networks of LeNet on MNIST database and ConvNet on CIFAR-10 database, our experiments show significant reduction of crossbar and routing area in NCS designs. Without accuracy loss, rank clipping reduces the total crossbar area to 13.62 or 51.81 in the NCS design of LeNet or ConvNet, respectively. The following group connection deletion further decreases the routing area of LeNet or ConvNet to 8.1 or 52.06 , respectively. We study the time-complexity of robust atomic read write storage from fault-prone storage components in asynchronous message-passing systems. Robustness here means wait-free tolerating the largest possible number t of Byzantine storage component failures (optimal resilience) without relying on data authentication. We show that no single-writer multiple-reader (SWMR) robust atomic storage implementation exists if (a) read operations complete in less than four communication round-trips (rounds), and (b) the time complexity of write operations is constant. More precisely, we present two lower bounds. The first is a read lower bound stating that three rounds of communication are necessary to read from a SWMR robust atomic storage. The second is a write lower bound, showing that Ω(log(t)) write rounds are necessary to read in three rounds from such a storage. Applied to known results, our lower bounds close a fundamental gap: we show that time-optimal robust atomic storage can be obtained using well-known transformations from regular to atomic storage and existing time-optimal regular storage implementations. This paper proposes CoNoC (Contention-free optical NoC) as a new architecture for on-chip routing of optical packets. CoNoC is built upon all-optical switches (AOSs) which passively route optical data streams based on their wavelengths. The key idea of the proposed architecture is the utilization of per-receiver wavelength in the data network to prevent optical contention at the intermediate nodes. Routing optical packets according to their wavelength eliminates the need for resource reservation at the intermediate nodes and the corresponding latency, power, and area overheads. Since passive architecture of the AOS confines the optical contention to the end-points, we propose an electrical arbitration architecture for resolving optical contention at the destination nodes. By performing a series of simulations, we study the efficiency of the proposed architecture, its power and energy consumption, and the data transmission latency. Moreover, we compare the proposed architecture with electrical NoCs and alternative ONoC architectures under various synthetic traffic patterns. Averaged across different traffic patterns, the proposed architecture reduces per-packet power consumption by 19 , 28 , 29 , and 91 and achieves per-packet energy reduction of 28 , 40 , 20 , and 99 over Columbia, Phastlane, @l-router, and electrical torus, respectively. We expect that many-core microprocessors will push performance per chip from the 10 gigaflop to the 10 teraflop range in the coming decade. To support this increased performance, memory and inter-core bandwidths will also have to scale by orders of magnitude. Pin limitations, the energy cost of electrical signaling, and the non-scalability of chip-length global wires are significant bandwidth impediments. Recent developments in silicon nanophotonic technology have the potential to meet these off- and on-stack bandwidth requirements at acceptable power levels. Corona is a 3D many-core architecture that uses nanophotonic communication for both inter-core communication and off-stack communication to memory or I O devices. Its peak floating-point performance is 10 teraflops. Dense wavelength division multiplexed optically connected memory modules provide 10 terabyte per second memory bandwidth. A photonic crossbar fully interconnects its 256 low-power multithreaded cores at 20 terabyte per second bandwidth. We have simulated a 1024 thread Corona system running synthetic benchmarks and scaled versions of the SPLASH-2 benchmark suite. We believe that in comparison with an electrically-connected many-core alternative that uses the same on-stack interconnect power, Corona can provide 2 to 6 times more performance on many memory intensive workloads, while simultaneously reducing power. Routers built around a single-stage crossbar and a centralized scheduler do not scale, and (in practice) do not provide the throughput guarantees that network operators need to make efficient use of their expensive long-haul links. In this paper we consider how optics can be used to scale capacity and reduce power in a router. We start with the promising load-balanced switch architecture proposed by C-S. Chang. This approach eliminates the scheduler, is scalable, and guarantees 100 throughput for a broad class of traffic. But several problems need to be solved to make this architecture practical: (1) Packets can be mis-sequenced, (2) Pathological periodic traffic patterns can make throughput arbitrarily small, (3) The architecture requires a rapidly configuring switch fabric, and (4) It does not work when linecards are missing or have failed. In this paper we solve each problem in turn, and describe new architectures that include our solutions. We motivate our work by designing a 100Tb s packet-switched router arranged as 640 linecards, each operating at 160Gb s. We describe two different implementations based on technology available within the next three years. Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique. Implementation of Neuromorphic Systems using post Complementary Met al-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks. However, the recent trend to design Deep Neural Networks (DNNs) for achieving human-like cognitive abilities poses significant challenges towards the scalable design of neuromorphic systems (due to the increase in computation storage demands). Network pruning [7] is a powerful technique to remove redundant connections for designing optimally connected (maximally sparse) DNNs. However, such pruning techniques induce irregular connections that are incoherent to the crossbar structure. Eventually they produce DNNs with highly inefficient hardware realizations (in terms of area and energy). In this work, we propose TraNNsformer - an integrated training framework that transforms DNNs to enable their efficient realization on MCA-based systems. TraNNsformer first prunes the connectivity matrix while forming clusters with the remaining connections. Subsequently, it retrains the network to fine tune the connections and reinforce the clusters. This is done iteratively to transform the original connectivity into an optimally pruned and maximally clustered mapping. We evaluated the proposed framework by transforming different Multi-Layer Perceptron (MLP) based Spiking Neural Networks (SNNs) on a wide range of datasets (MNIST, SVHN and CIFAR10) and executing them on MCA-based systems to analyze the area and energy benefits. Without accuracy loss, TraNNsformer reduces the area (energy) consumption by 28 - 55 (49 - 67 ) with respect to the original network. Compared to network pruning, TraNNsformer achieves 28 - 49 (15 - 29 ) area (energy) savings. Furthermore, TraNNsformer is a technology-aware framework that allows mapping a given DNN to any MCA size permissible by the memristive technology for reliable operations. A range of new datacenter switch designs combine wireless or optical circuit technologies with electrical packet switching to deliver higher performance at lower cost than traditional packet-switched networks. These "hybrid" networks schedule large traffic demands via a high-rate circuits and remaining traffic with a lower-rate, traditional packet-switches. Achieving high utilization requires an efficient scheduling algorithm that can compute proper circuit configurations and balance traffic across the switches. Recent proposals, however, provide no such algorithm and rely on an omniscient oracle to compute optimal switch configurations. Finding the right balance of circuit and packet switch use is difficult: circuits must be reconfigured to serve different demands, incurring non-trivial switching delay, while the packet switch is bandwidth constrained. Adapting existing crossbar scheduling algorithms proves challenging with these constraints. In this paper, we formalize the hybrid switching problem, explore the design space of scheduling algorithms, and provide insight on using such algorithms in practice. We propose a heuristic-based algorithm, Solstice that provides a 2.9× increase in circuit utilization over traditional scheduling algorithms, while being within 14 of optimal, at scale. Convolutional Neural Network (CNN) is a powerful technique widely used in computer vision area, which also demands much more computations and memory resources than traditional solutions. The emerging met al-oxide resistive random-access memory (RRAM) and RRAM crossbar have shown great potential on neuromorphic applications with high energy efficiency. However, the interfaces between analog RRAM crossbars and digital peripheral functions, namely Analog-to-Digital Converters (AD-Cs) and Digital-to-Analog Converters (DACs), consume most of the area and energy of RRAM-based CNN design due to the large amount of intermediate data in CNN. In this paper, we propose an energy efficient structure for RRAM-based CNN. Based on the analysis of data distribution, a quantization method is proposed to transfer the intermediate data into 1 bit and eliminate DACs. An energy efficient structure using input data as selection signals is proposed to reduce the ADC cost for merging results of multiple crossbars. The experimental results show that the proposed method and structure can save 80 area and more than 95 energy while maintaining the same or comparable classification accuracy of CNN on MNIST. We consider broadcasting from a fault-free source to all nodes of a completely connected n-node network in the presence of k faulty nodes. Every node can communicate with at most one other node in a unit of time and during this period every pair of communicating nodes can exchange information packets. Faulty nodes cannot send information. Broadcasting is adaptive, i.e., a node schedules its next communication on the basis of information currently available to it. Assuming that the fraction of faulty nodes is bounded by a constant smaller than 1, we construct a broadcasting algorithm working in worst-case time O(log2 n). Future chip multi-processors (CMPs) will require high-performance yet low-energy networks-on-chip to provide scalable communication for the increasing number of cores. CMOS-compatible nanophotonic interconnects have recently emerged as a promising candidate for replacing traditional electrical network-on-chip, by providing high bandwidth chip-wide communication at low latency and power. In this paper, we utilize the emerging nanophotonic technology and propose RCBus, an optical network-on-chip topology in which multiple nanophotonic buses are uniformly distributed in each row and column of a two-dimensional torus network. Each bus employs a dense-wavelength-division-multiplexing optical waveguide to enable high-bandwidth express traversal along each dimension. We use multiple write single-read schemes and virtual channel based token protocol to implement light-speed arbitration and flow control. The optical-electrical interface is also studied in the paper. The proposed optical network-on-chip can exploit the advantages of both photonic bus and direct torus topology. Simulation results under synthetic and real-world application traffic show that RCBus is 2X better in terms of performance than the electrical mesh network-on-chip while saving nearly 30 power, and achieves on average 24 throughput improvement at much lower cost compared to the state-of-the-art optical network. DOI: http: dx.doi.org 10.5755 j01.eee.18.8.2633 A method for estimating unknown node positions in a sensor network based exclusively on connectivity-induced constraints is described. Known peer-to-peer communication in the network is modeled as a set of geometric constraints on the node positions. The global solution of a feasibility problem for these constraints yields estimates for the unknown positions of the nodes in the network. Providing that the constraints are tight enough, simulation illustrates that this estimate becomes close to the actual node positions. Additionally, a method for placing rectangular bounds around the possible positions for all unknown nodes in the network is given. The area of the bounding rectangles decreases as additional or tighter constraints are included in the problem. Specific models are suggested and simulated for isotropic and directional communication, representative of broadcast-based and optical transmission respectively, though the methods presented are not limited to these simple cases. The ever-increasing bandwidth requirements of modern datacenters have led researchers to propose networks based upon optical circuit switches, but these proposals face significant deployment challenges. In particular, previous proposals dynamically configure circuit switches in response to changes in workload, requiring network-wide demand estimation, centralized circuit assignment, and tight time synchronization between various network elements--- resulting in a complex and unwieldy control plane. Moreover, limitations in the technologies underlying the individual circuit switches restrict both the rate at which they can be reconfigured and the scale of the network that can be constructed. We propose RotorNet, a circuit-based network design that addresses these two challenges. While RotorNet dynamically reconfigures its constituent circuit switches, it decouples switch configuration from traffic patterns, obviating the need for demand collection and admitting a fully decentralized control plane. At the physical layer, RotorNet relaxes the requirements on the underlying circuit switches---in particular by not requiring individual switches to implement a full crossbar---enabling them to scale to 1000s of ports. We show that RotorNet outperforms comparably priced Fat Tree topologies under a variety of workload conditions, including traces taken from two commercial datacenters. We also demonstrate a small-scale RotorNet operating in practice on an eight-node testbed. Error correction for polynomial block codes is achieved without prior evaluation of power sum symmetric functions. The received word R (z) is reduced mod G (z), the generator of the code and a function F (z) of error locator polynomial W(z), errata values Y and code dependent functions f(xi) of the error positions xi given by ##EQU1## is decomposed into a rational polynomial function N (z) W (z) for which deg (N (z) )<deg ( W (z) )<number of correctable errors. W (z) is the error locator polynomial, the roots of which are the errata locations X and Y, the correction to the received character is obtained from ##EQU2## evaluated at Xi using non-erased check symbols of R (z). Correction is carried out in a crossbar switch structure which recalls a stored copy of R (z) and corrects bits as specified by (Xi, Yi). Another embodiment interposes a matrix transform to transform the symbols of the received word so as to treat a selected set of symbols as erased checks and to present error location corrections directly to the crossbar. Only when changes occur in the pattern of errata is this error corrector apparatus required to operate and to redetermine the transform executed on incoming data R (z). Distributed cloud applications, including batch processing, streaming, and scale-out databases, generate a significant amount of network traffic and a considerable fraction of their runtime is due to network activity. This paper initiates the study of deterministic algorithms for collocating frequently communicating nodes in a distributed networked systems in an online fashion. In particular, we introduce the Balanced RePartitioning (BRP) problem: Given an arbitrary sequence of pairwise communication requests between n nodes, with patterns that may change over time, the objective is to dynamically partition the nodes into ( ) clusters, each of size k, at a minimum cost. Every communication request needs to be served: if the communicating nodes are located in the same cluster, the request is served locally, at cost 0; if the nodes are located in different clusters, the request is served remotely using inter-cluster communication, at cost 1. The partitioning can be updated dynamically (i.e., repartitioned), by migrating nodes between clusters at cost ( ) per node migration. The goal is to devise online algorithms which find a good trade-off between the communication and the migration cost, i.e., “rent” or “buy”, while maintaining partitions which minimize the number of inter-cluster communications. BRP features interesting connections to other well-known online problems. In particular, we show that scenarios with ( =2 ) generalize online paging, and scenarios with (k=2 ) constitute a novel online version of maximum matching. We consider settings both with and without cluster-size augmentation. Somewhat surprisingly (and unlike online paging), we prove that any deterministic online algorithm has a competitive ratio of at least k, even with augmentation. Our main technical contribution is an (O(k k ) )-competitive deterministic algorithm for the setting with (constant) augmentation. This is attractive as, in contrast to ( ), k is likely to be small in practice. For the case of matching ( (k=2 )), we present a constant competitive algorithm that does not rely on augmentation. We develop and implement an optimal broadcast algorithm for fully connected processor networks under a bidirectional communication model in which each processor can simultaneously send a message to one processor and receive a message from another, possibly different processor. For any number of processors p the algorithm requires [email protected][email protected]? communication rounds to broadcast N blocks of data from a root processor to the remaining processors, meeting the lower bound in the model. For data of size m, assuming that sending and receiving data of size m^' takes time @[email protected]^', the best running time that can be achieved by the division of m into equal-sized blocks is ((@[email protected]?-1)@[email protected])^2. The algorithm uses a regular, circulant graph communication pattern, and degenerates into a binomial tree broadcast when the number of blocks to be broadcast is one. The algorithm is furthermore well suited to fully connected clusters of SMP (Symmetric Multi-Processor) nodes. The algorithm is implemented as part of an MPI (Message Passing Interface) library. We demonstrate significant practical bandwidth improvements of up to a factor 1.5 over several other, commonly used broadcast algorithms on both a small SMP cluster and a 72 node NEC SX vector supercomputer. Communication systems with all-optical multicasting have better performance than those using optical electrical optical conversion. Multicast protocols assume that all nodes in the network can forward the signal from one input to several outputs. Since fabrication of an optical switch with splitting capability is an expensive technology, there are few switches that are multicast capable. The heuristics designed for all-optical networks have to handle this limitation. We introduce a shortest path based forest algorithm for all-optical networks. We propose a post-processing algorithm to reduce the number of wavelengths needed. The effect of this post-processing algorithm is also examined in a well-known approach.