This paper analyzes the scalability in arrayed waveguide grating router (AWGR)-based interconnect architectures and demonstrates active AWGR-based switching using a distributed control plane. First, the paper analyses an all-to-all single AWGR passive interconnection with $N$ nodes and proposes a new architecture that overcomes the scalability limitation given by wavelength registration and crosstalk, by introducing multiples of smaller AWGRs $(W$ × $W)$ operating on a fewer number of wavelengths $(W < N)$. Second, this paper demonstrates active AWGR switching with a distributed control plane, to be used when the size of the interconnection network makes the all-to-all approach using passive AWGRs impractical. In particular, an active AWGR-based TONAK switch is introduced. TONAK combines an all-optical NACK technique, which removes the need for electrical buffers at the switch input/output ports, and a TOKEN technique, which enables a distributed all-optical arbiter to handle packet contention. The experimental validation and performance study of the AWGR-based TONAK switch is presented, demonstrating the feasibility of the TONAK solution and the high throughput and low average packet latency for an up to 75% offered load. © 2013 IEEE.

Designing a graph processing system that can scale to graph sizes that are orders of magnitude larger than what is possible on a single accelerator requires a careful codesign of accelerator memory bandwidth and capacity, the interconnect bandwidth between accelerators, and the overall system architecture. We present a high-level bottleneck-analysis model for design and evaluation of scalable and balanced accelerators for graph processing. We show several applications of this model including how to choose the right mix of different memory types, network topology, network bisection bandwidth, and system-level architecture to match the access patterns and capacity requirements of different data structures for a given graph and a performance target.

Progress in computing architectures is approaching a paradigm shift: traditional computing based on digital complementary metal-oxide semiconductor technology is nearing physical limits in terms of miniaturization, speed, and, especially, power consumption. Consequently, alternative approaches are under investigation. One of the most promising is based on a "brain-like" or neuromorphic computation scheme. Another approach is quantum computing using photons. Both of these approaches can be realized using silicon photonics, and at the heart of both technologies is an efficient, ultra-low power broad band optical modulator. As silicon modulators suffer from relatively high power consumption, materials other than silicon itself have to be considered for the modulator. In this Perspective, we present our view on such materials. We focus on oxides showing a strong linear electro-optic effect that can also be integrated with Si, thus capitalizing on new materials to enable the devices and circuit architectures that exploit shifting computational machine learning paradigms, while leveraging current manufacturing infrastructure. This is expected to result in a new generation of computers that consume less power and possess a larger bandwidth.