Scaling Photonic Neural Networks: A Silicon Photonic GeMM Leveraging a Time-space Multiplexed Xbar
Stefanos Kovaios, Ioannis Roumpos, Miltiadis Moralis‐Pegios, George Giamougiannis, Mathias Berciano, Filippo Ferraro, Dieter Bode, Srinivasan Ashwyn Srinivasan, Marianna Pantouvaki, Nikos Pleros, Apostolos Tsakyridis · Journal of Lightwave Technology · 2024
We demonstrate a silicon photonic Xbar-based general matrix multiplier (Xbar GeMM) for optical neural network (NN) applications, utilizing a hybrid time-space multiplexing scheme for supporting matrix dimensions far beyond the dimensions of the Xbar circuit. We present the operational principle of the silicon photonic accelerator that is capable of merging space and time division multiplexing techniques through the use of high-speed input and weighting nodes within a coherentM×NXbar. The proposed scheme was demonstrated experimentally using a 2 × 2 Xbar that employs electro-absorption modulators (EAM) with 56 GHz bandwidth both at its input signal vector and its weight matrix modulation stages. Its experimental validation as a photonic GeMM engine was performed for 5, 10, 20, 30 and 50 GBd compute rates and was benchmarked as a NN classifier for the IRIS dataset, successfully executing a total number of 2100 products over a 2 × 2 matrix hardware with an accuracy up to 93.3%. All SiGe EAMs were driven by high-speed electrical signals with a peak-to-peak voltage ranging between 0.9–1.2 V, suggesting a strong potential for a photonic engine that will be capable to perform with CMOS-compatible driving voltages. Finally, we discuss the pros and cons of the proposed hybrid multiplexing scheme, concluding to a thorough system performance and energy efficiency analysis.