Abstract
AbstractThe technologically-relevant task of feature extraction from data performed in deep-learning systems is routinely accomplished as repeated fast Fourier transforms (FFT) electronically in prevalent domain-specific architectures such as in graphics processing units (GPU). However, electronics systems are limited with respect to power dissipation and delay, due to wire-charging challenges related to interconnect capacitance. Here we present a silicon photonics-based architecture for convolutional neural networks that harnesses the phase property of light to perform FFTs efficiently by executing the convolution as a multiplication in the Fourier-domain. The algorithmic executing time is determined by the time-of-flight of the signal through this photonic reconfigurable passive FFT ‘filter’ circuit and is on the order of 10’s of picosecond short. A sensitivity analysis shows that this optical processor must be thermally phase stabilized corresponding to a few degrees. Furthermore, we find that for a small sample number, the obtainable number of convolutions per {time, power, and chip area) outperforms GPUs by about two orders of magnitude. Lastly, we show that, conceptually, the optical FFT and convolution-processing performance is indeed directly linked to optoelectronic device-level, and improvements in plasmonics, metamaterials or nanophotonics are fueling next generation densely interconnected intelligent photonic circuits with relevance for edge-computing 5G networks by processing tensor operations optically.
Highlights
In this post-Moore era the trend in signal processing and computing towards a higher degree of compute-system heterogeneity, specialized and domain-specific processors are gaining interest [1] such as exemplary GPU’s augmenting CPU systems, or Tensor core Processor Units (TPU) outperforming GPU’s on specific tasks [2]
We show that, conceptually, the optical FFT and convolution-processing performance is directly linked to optoelectronic device-level, and improvements in plasmonics, metamaterials or nanophotonics are fueling generation densely interconnected intelligent photonic circuits with relevance for edge-computing 5G networks by processing tensor operations optically
Ahmed et al.: Photonic tensor operations towards efficient and high-speed neural networks performed by optical components, d) no capacitive wire charging synergistic with nonVan Neumann distributed architectures such as NNs
Summary
In this post-Moore era the trend in signal processing and computing towards a higher degree of compute-system heterogeneity, specialized and domain-specific processors are gaining interest [1] such as exemplary GPU’s augmenting CPU systems, or Tensor core Processor Units (TPU) outperforming GPU’s on specific tasks [2]. M. Ahmed et al.: Photonic tensor operations towards efficient and high-speed neural networks performed by optical components (this is used in this work), d) no capacitive wire charging synergistic with nonVan Neumann distributed architectures such as NNs. Challenges of optics, include e) ensuring sufficient signal-to-noise (SNR) due to the analog nature, f) nonexistence of nonvolatility (yet this can be introduced, for instance, via phase-change materials), g) weak and bulky electro-optic conversion, and h) system packing complexity arising from alignment requirements.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
