Nanonntennas are critical elements of nanoscale wireless communication technologies with potential to overcome some of the limitations of on-chip interconnects. In this paper, we model the optical response of graphene-based nanoantennas (GNAs) using an equivalent resistive-inductive-capacitive (RLC) circuit by incorporating plasmonic effects that are present in graphene at terahertz (THz) frequencies. The equivalent circuit model is used to estimate the resonance characteristics of the nanoantenna, thereby facilitating geometry and material optimization. Three GNA structures are considered, namely bowtie, circular, and rectangular dimers with vacuum and silicon dioxide dielectric environment. We characterize the radiation efficiency, the Purcell factor, the quality factor, and the resonant frequency of various antennas with respect to the physical properties of the surrounding dielectric media and the graphene sheet. The GNAs are designed for a resonant frequency in the range of 10 − 25 THz with field enhancements around 10 4 − 10 5 and radiation efficiency ∼ 5 − 16%. The circuit model is applied to examine the trade-off between the radiation efficiency and the field enhancement in the antenna gap. While bowtie antennas have higher field enhancement due to charge accumulation in their pointed structure, they suffer from low radiation efficiency stemming from the large spreading resistance losses. The circuit model is validated against finite-difference time-domain (FDTD) simulations conducted in Lumerical, and excellent match between the model and numerical results is demonstrated in the THz regime. The circuit models can be readily used in a hierarchical circuit simulator to design and optimize optical nanoantennas for THz communication in high-performance computing systems.
Machine learning model weights and activations are represented in full-precision during training. This leads to performance degradation in runtime when deployed on neural network accelerator (NNA) chips, which leverage highly parallelized fixed-point arithmetic to improve runtime memory and latency. In this work, we replicate the NNA operators during the training phase, accounting for the degradation due to low-precision inference on the NNA in back-propagation. Our proposed method efficiently emulates NNA operations, thus foregoing the need to transfer quantization error-prone data to the Central Processing Unit (CPU), ultimately reducing the user perceived latency (UPL). We apply our approach to Recurrent Neural Network-Transducer (RNN-T), an attractive architecture for on-device streaming speech recognition tasks. We train and evaluate models on 270K hours of English data and show a 5-7% improvement in engine latency while saving up to 10% relative degradation in WER.
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