Photonic computing shows promise for transformative advancements in machine
learning (ML) acceleration, offering ultra-fast speed, massive parallelism, and
high energy efficiency. However, current photonic tensor core (PTC) designs
based on standard optical components hinder scalability and compute density due
to their large spatial footprint. To address this, we propose an ultra-compact
PTC using customized programmable multi-operand multimode interference (MOMMI)
devices, named M3ICRO. The programmable MOMMI leverages the intrinsic light
propagation principle, providing a single-device programmable matrix unit
beyond the conventional computing paradigm of one multiply-accumulate (MAC)
operation per device. To overcome the optimization difficulty of customized
devices that often requires time-consuming simulation, we apply ML for optics
to predict the device behavior and enable a differentiable optimization flow.
We thoroughly investigate the reconfigurability and matrix expressivity of our
customized PTC, and introduce a novel block unfolding method to fully exploit
the computing capabilities of a complex-valued PTC for near-universal
real-valued linear transformations. Extensive evaluations demonstrate that
M3ICRO achieves a 3.4-9.6x smaller footprint, 1.6-4.4x higher speed, 10.6-42x
higher compute density, 3.7-12x higher system throughput, and superior noise
robustness compared to state-of-the-art coherent PTC designs, while maintaining
close-to-digital task accuracy across various ML benchmarks. Our code is
open-sourced at this https URL

提出了一种名为 M3ICRO 的基于多路多模干涉（MOMMI）器件定制的可编程光子张量芯片，在充分利用光电计算的能力的同时具有高能效、小体积以及优异的噪声鲁棒性。