Recurrent Neural Networks (RNNs) are used in applications that learn dependencies in data
sequences, such as speech recognition, human activity recognition, and anomaly detection …

Silicon nanophotonics, with its high-speed, low-loss optical interconnects, and high
computation capabilities, is seen as one of the promising technologies that can easily …

2.5 D chiplet systems have been proposed to improve the low manufacturing yield of large-
scale chips. However, connecting the chiplets through an electronic interposer imposes a …

Parameter quantization in convolutional neural networks (CNNs) can help generate efficient
models with lower memory footprint and computational complexity. But, homogeneous …

This paper proposes a high-performance and energy-efficient optical near-sensor
accelerator for vision applications, called Lightator. Harnessing the promising efficiency …

The surging demand for machine learning (ML) applications has emphasized the pressing
need for efficient ML accelerators capable of addressing the computational and energy …

2.5 D chiplet systems have showcased low manufacturing costs and modular designs for
machine learning (ML) acceleration. Nevertheless, communication challenges arise from …

Abstract Optical Network on Chip (ONoC) is now considered a promising alternative to
traditional electrical interconnects. Meanwhile, several challenges such as temperature and …

Targeting vision applications at the edge, in this work, we systematically explore and
propose a high-performance and energy-efficient Optical In-Sensor Accelerator architecture …

Federated learning (FL) has emerged as a distributed machine learning (ML) technique that
can protect local data privacy for participating clients and improve system efficiency. Instead …