Photonic tensor cores for machine learning

Volker J. Sorger

doi:10.1117/12.2568234

20 August 2020 Photonic tensor cores for machine learning

Volker J. Sorger

Proceedings Volume 11469, Emerging Topics in Artificial Intelligence 2020; 114690Q (2020) https://doi.org/10.1117/12.2568234
Event: SPIE Nanoscience + Engineering, 2020, Online Only

Abstract

Here we introduced an integrated photonics-based TPU by strategically utilizing a) photonic parallelism via wavelength division multiplexing, b) high 2 Peta-operations-per second throughputs enabled by 10’s of picosecond-short delays from optoelectronics and compact photonic integrated circuitry, and c) zero power-consuming novel photonic multi-state memories based on phase-change materials featuring vanishing losses in the amorphous state. Combining these physical synergies of material, function, and system, we show that the performance of this 8-bit photonic TPU can be 2-3 orders higher compared to an electrical TPU whilst featuring similar chip areas. The runtime complexity is O(1) once the kernel matrix is programmed, however the engine scales with O(N^3) resources (devices). This system could ultimately perform in the range 10-500fJ/MAC, 1-50 TMACs/mm^2, and ~100ps (1 clock cycle) per VMM operation.

Conference Presentation

Citation Download Citation

Volker J. Sorger "Photonic tensor cores for machine learning", Proc. SPIE 11469, Emerging Topics in Artificial Intelligence 2020, 114690Q (20 August 2020); https://doi.org/10.1117/12.2568234

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