Scalability of broadcast performance in wireless network-on-chip

Networks-on-Chip (NoCs) are currently the paradigm of choice to interconnect the cores of a chip multiprocessor. However, conventional NoCs may not suffice to fulfill the on-chip communication requirements of processors with hundreds or thousands of cores. The main reason is that the performance of such networks drops as the number of cores grows, especially in the presence of multicast and broadcast traffic. This not only limits the scalability of current multiprocessor architectures, but also sets a performance wall that prevents the development of architectures that generate moderate-to-high levels of multicast. In this paper, a Wireless Network-on-Chip (WNoC) where all cores share a single broadband channel is presented. Such design is conceived to provide low latency and ordered delivery for multicast/broadcast traffic, in an attempt to complement a wireline NoC that will transport the rest of communication flows. To assess the feasibility of this approach, the network performance of WNoC is analyzed as a function of the system size and the channel capacity, and then compared to that of wireline NoCs with embedded multicast support. Based on this evaluation, preliminary results on the potential performance of the proposed hybrid scheme are provided, together with guidelines for the design of MAC protocols for WNoC.Peer ReviewedPostprint (published version

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