Corona: System Implications of Emerging Nanophotonic Technology

TL;DR

This paper proposes Corona, a 3D many-core architecture utilizing nanophotonic communication to significantly enhance bandwidth and performance while reducing power consumption in future high-performance microprocessors.

Contribution

It introduces a novel nanophotonic-based 3D many-core system architecture with integrated optical communication for inter-core and off-stack memory access, demonstrating substantial performance and power benefits.

Findings

Corona achieves 10 teraflops peak performance.

Memory bandwidth reaches 10 terabytes per second.

Simulation shows 2 to 6 times performance improvement over electrical interconnects.

Abstract

We expect that many-core microprocessors will push performance per chip from the 10 gigaflop to the 10 teraflop range in the coming decade. To support this increased performance, memory and inter-core bandwidths will also have to scale by orders of magnitude. Pin limitations, the energy cost of electrical signaling, and the non-scalability of chip-length global wires are significant bandwidth impediments. Recent developments in silicon nanophotonic technology have the potential to meet these off- and on- stack bandwidth requirements at acceptable power levels. Corona is a 3D many-core architecture that uses nanophotonic communication for both inter-core communication and off-stack communication to memory or I/O devices. Its peak floating-point performance is 10 teraflops. Dense wavelength division multiplexed optically connected memory modules provide 10 terabyte per second memory bandwidth. A photonic crossbar fully interconnects its 256 low-power multithreaded cores at 20 terabyte per second bandwidth. We have simulated a 1024 thread Corona system running synthetic benchmarks and scaled versions of the SPLASH-2 benchmark suite. We believe that in comparison with an electrically-connected many-core alternative that uses the same on-stack interconnect power, Corona can provide 2 to 6 times more performance on many memory-intensive workloads, while simultaneously reducing power.