A performance model of communication in the quarc NoC

Networks on-chip (NoC) emerged as a promising communication medium for future MPSoC development. To serve this purpose, the NoCs have to be able to efficiently exchange all types of traffic including the collective communications at a reasonable cost. The Quarc NoC is introduced as a NOC which is highly efficient in performing collective communication operations such as broadcast and multicast. This paper presents an introduction to the Quarc scheme and an analytical model to compute the average message latency in the architecture. To validate the model we compare the model latency prediction against the results obtained from discrete-event simulations

Quarc: a novel network-on-chip architecture

This paper introduces the Quarc NoC, a novel NoC architecture inspired by the Spidergon NoC. The Quarc scheme significantly outperforms the Spidergon NoC through balancing the traffic which is the result of the modifications applied to the topology and the routing elements.The proposed architecture is highly efficient in performing collective communication operations including broadcast and multicast. We present the topology, routing discipline and switch architecture for the Quarc NoC and demonstrate the performance with the results obtained from discrete event simulations

Design and implementation of the Quarc network on-chip

Networks-on-Chip (NoC) have emerged as alternative to buses to provide a packet-switched communication medium for modular development of large Systems-on-Chip. However, to successfully replace its predecessor, the NoC has to be able to efficiently exchange all types of traffic including collective communications. The latter is especially important for e.g. cache updates in multicore systems. The Quarc NoC architecture has been introduced as a Networks-on-Chip which is highly efficient in exchanging all types of traffic including broadcast and multicast. In this paper we present the hardware implementation of the switch architecture and the network adapter (transceiver) of the Quarc NoC. Moreover, the paper presents an analysis and comparison of the cost and performance between the Quarc and the Spidergon NoCs implemented in Verilog targeting the Xilinx Virtex FPGA family. We demonstrate a dramatic improvement in performance over the Spidergon especially for broadcast traffic, at no additional hardware cost

A performance model of multicast communication in wormhole-routed networks on-chip

Collective communication operations form a part of overall traffic in most applications running on platforms employing direct interconnection networks. This paper presents a novel analytical model to compute communication latency of multicast as a widely used collective communication operation. The novelty of the model lies in its ability to predict the latency of the multicast communication in wormhole-routed architectures employing asynchronous multi-port routers scheme. The model is applied to the Quarc NoC and its validity is verified by comparing the model predictions against the results obtained from a discrete-event simulator developed using OMNET++

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

MorphIC: A 65-nm 738k-Synapse/mm2^2 Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning

Recent trends in the field of neural network accelerators investigate weight quantization as a means to increase the resource- and power-efficiency of hardware devices. As full on-chip weight storage is necessary to avoid the high energy cost of off-chip memory accesses, memory reduction requirements for weight storage pushed toward the use of binary weights, which were demonstrated to have a limited accuracy reduction on many applications when quantization-aware training techniques are used. In parallel, spiking neural network (SNN) architectures are explored to further reduce power when processing sparse event-based data streams, while on-chip spike-based online learning appears as a key feature for applications constrained in power and resources during the training phase. However, designing power- and area-efficient spiking neural networks still requires the development of specific techniques in order to leverage on-chip online learning on binary weights without compromising the synapse density. In this work, we demonstrate MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning rule and a hierarchical routing fabric for large-scale chip interconnection. The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF) neurons and more than two million plastic synapses for an active silicon area of 2.86mm$^2$ in 65nm CMOS, achieving a high density of 738k synapses/mm$^2$. MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy tradeoff on the MNIST classification task compared to previously-proposed SNNs, while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE Transactions on Biomedical Circuits and Systems journal (2019), the fully-edited paper is available at https://ieeexplore.ieee.org/document/876400

A communication model of broadcast in wormhole-routed networks on-chip

This paper presents a novel analytical model to compute communication latency of broadcast as the most fundamental collective communication operation. The novelty of the model lies in its ability to predict the broadcast communication latency in wormhole-routed architectures employing asynchronous multi-port routers scheme. The model is applied to the Quarc NoC and its validity is verified by comparing the model predictions against the results obtained from a discrete-event simulator developed using OMNET++