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Amir Begović

Publications and source records attributed to Amir Begović.

2 recordsLinked to original sources

Silicon Photonics Foundry Fabricated, Slow-Light Enhanced, Low Power Thermal Phase Shifter

In this research, we developed a low-power silicon photonics foundry-fabricated slow-light thermal phase shifter (SLTPS) where the slow-light (SL) effect is achieved using an integrated Bragg grating (BG) waveguide. Heating the grating induces a red shift in the transmission spectrum, leading to an increased group index $n_g$ during operation, which facilitates a further reduction in the voltage needed for a $π$ phase shift, i.e. $V_π$. Additionally, we investigated a compact Mach-Zehnder Interferometer (MZI) that incorporates the SLTPS in both arms with a phase shifter length of 50 $μ$m. A detailed theoretical analysis was conducted to address the non-idealities of the SL-MZI due to uneven optical power splitting and unbalanced loss in the two MZI arms. The $V_π$ and power consumption for a $π$ phase shift $(P_π)$ of the SL-MZI were quantified for operation in the slow light regime, demonstrating a $V_π$ of 1.1 V and a $P_π$ of 3.63 mW at an operational wavelength near the photonic band edge. The figure of merit (FOM) $P_π \times τ$ is commonly used to assess the performance of thermal optical switches. The SL-MZI in this work has achieved a low $P_π \times τ$ of 5.1 mW $μ$s. Insertion loss of the SL-MZI ranges from 1.3 dB to 4.4 dB depending on the operation wavelength, indicating a trade-off with the $V_π$ reduction.

physics.optics↗

TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator

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.

cs.ET↗