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.