4 papers
LUMION: Fast Fault Recovery for ML Jobs Using Programmable Optical Fabrics
Abhishek Vijaya Kumar, Eric Ding, Arjun Devraj +2
When accelerators fail in modern ML datacenters, operators migrate the affected ML training or inference jobs to entirely new racks. This approach, while preserving network perform…
Morphlux: Transforming Torus Fabrics for Efficient Multi-tenant ML
Abhishek Vijaya Kumar, Eric Ding, Arjun Devraj +2
We develop Morphlux, a server-scale programmable photonic fabric to interconnect accelerators within servers. We show that augmenting state-of-the-art torus-based ML data-centers w…
Chip-to-chip photonic connectivity in multi-accelerator servers for ML
Abhishek Vijaya Kumar, Arjun Devraj, Darius Bunandar +1
We present a rack-scale compute architecture for ML using multi-accelerator servers connected via chip-to-chip silicon photonic components. Our architecture achieves (1) multi-tena…
Towards Efficient Hyperdimensional Computing Using Photonics
Farbin Fayza, Cansu Demirkiran, Hanning Chen +8
Over the past few years, silicon photonics-based computing has emerged as a promising alternative to CMOS-based computing for Deep Neural Networks (DNN). Unfortunately, the non-lin…