  The bandwidth and energy demands of neural networks has spurred tremendous
interest in developing novel neuromorphic hardware, including photonic
integrated circuits. Although an optical waveguide can accommodate hundreds of
channels with THz bandwidth, the channel count of photonic systems is always
bottlenecked by the devices within. In WDM-based photonic neural networks, the
synapses, i.e. network interconnections, are typically realized by microring
resonators (MRRs), where the WDM channel count (N) is bounded by the
free-spectral range of the MRRs. For typical Si MRRs, we estimate N <= 30
within the C-band. This not only restrains the aggregate throughput of the
neural network but also makes applications with high input dimensions
unfeasible. We experimentally demonstrate that photonic crystal nanobeam based
synapses can be FSR-free within C-band, eliminating the bound on channel count.
This increases data throughput as well as enables applications with
high-dimensional inputs like natural language processing and high resolution
image processing. In addition, the smaller physical footprint of photonic
crystal nanobeam cavities offers higher tuning energy efficiency and a higher
compute density than MRRs. Nanophotonic cavity based synapse thus offers a path
towards realizing highly scalable photonic neural networks.
