  Electronic-photonic computing systems offer immense potential in
energy-efficient artificial intelligence (AI) acceleration tasks due to the
superior computing speed and efficiency of optics, especially for real-time,
low-energy deep neural network (DNN) inference tasks on resource-restricted
edge platforms. However, current optical neural accelerators based on
foundry-available devices and conventional system architecture still encounter
a performance gap compared to highly customized electronic counterparts. To
bridge the performance gap due to lack of domain specialization, we present a
time-multiplexed dynamic photonic tensor accelerator, dubbed TeMPO, with
cross-layer device/circuit/architecture customization. At the device level, we
present foundry-compatible, customized photonic devices, including a slow-light
electro-optic modulator with experimental demonstration, optical splitters, and
phase shifters that significantly reduce the footprint and power in input
encoding and dot-product calculation. At the circuit level, partial products
are hierarchically accumulated via parallel photocurrent aggregation,
lightweight capacitive temporal integration, and sequential digital summation,
considerably relieving the analog-to-digital conversion bottleneck. We also
employ a multi-tile, multi-core architecture to maximize hardware sharing for
higher efficiency. Across diverse edge AI workloads, TeMPO delivers
digital-comparable task accuracy with superior quantization/noise tolerance. We
achieve a 368.6 TOPS peak performance, 22.3 TOPS/W energy efficiency, and 1.2
TOPS/mm$^2$ compute density, pushing the Pareto frontier in edge AI hardware.
This work signifies the power of cross-layer co-design and domain-specific
customization, paving the way for future electronic-photonic accelerators with
even greater performance and efficiency.
