Reflecting the recent slow-down in Moore's law and the proliferation of artificial
intelligence/machine learning workloads, the performance and energy consumption of …

Many optical circuit switched data center networks (DCN) have been proposed in the last
decade to attain higher capacity and topology reconfigurability, though commercial adoption …

Recent advances in fast optical switching technology show promise in meeting the high
goodput and low latency requirements of datacenter networks (DCN). We present …

Dragonfly is a highly scalable, low-diameter, and cost-efficient network topology, which has
been adopted in new exascale High Performance Computing (HPC) systems. However …

Disaggregated computing has been widely investigated to support the continuous progress
in computing performance and overcome the slowdown of Moore's law. It involves a flexible …

The commoditization of machine learning is fuelling the demand for compute required to
both train large models and infer from them. At the same time, scaling the performance of …

The accelerated growth of data traffic in data centers (DCs) globally is driven by the
dominance of multiple emerging data-intensive applications hosted by edge/cloud DCs …

The increasing size and complexity of deep learning (DL) models have led to the wide
adoption of distributed training methods in datacenters (DCs) and high-performance …

Efficient data movement between nodes in a data center is essential for optimal performance
of distributed workloads. With advancements in computing interconnection and memory …

The network has become a bottleneck for generative artificial intelligence (GAI) jobs.
Accelerating GAI jobs in edge data centers using hybrid electrical/optical switch is …