arXiv subjects

Liam Shaughnessy

Publications and source records attributed to Liam Shaughnessy.

3 recordsLinked to original sources

arXiv2023

Creating high-contrast patterns in multiple-scattering media via wavefront shaping

Wavefront shaping allows focusing light through or inside strongly scattering media, but the background intensity also increases due to long-range correlations, reducing the target's contrast. By manipulating non-local intensity correlations of scattered waves in a disordered system with input wavefront shaping, we create high-contrast patterns behind strongly scattering media and targeted energy delivery into a diffusive system with minimal change in the surrounding intensity. These are achieved by introducing the contrast operator and the difference operator, and utilizing their eigenstates to maximize the target-to-background intensity contrast and energy difference. This work opens the door to coherent control of non-local effects in wave transport for practical applications.

physics.optics↗

arXiv2023

Nonlinear optical encoding enabled by recurrent linear scattering

Optical information processing and computing can potentially offer enhanced performance, scalability and energy efficiency. However, achieving nonlinearity-a critical component of computation-remains challenging in the optical domain. Here we introduce a design that leverages a multiple-scattering cavity to passively induce optical nonlinear random mapping with a continuous-wave laser at a low power. Each scattering event effectively mixes information from different areas of a spatial light modulator, resulting in a highly nonlinear mapping between the input data and output pattern. We demonstrate that our design retains vital information even when the readout dimensionality is reduced, thereby enabling optical data compression. This capability allows our optical platforms to offer efficient optical information processing solutions across applications. We demonstrate our design's efficacy across tasks, including classification, image reconstruction, keypoint detection and object detection, all of which are achieved through optical data compression combined with a digital decoder. In particular, high performance at extreme compression ratios is observed in real-time pedestrian detection. Our findings open pathways for novel algorithms and unconventional architectural designs for optical computing.

physics.optics↗

arXiv2019

The Recurrent Processing Unit: Hardware for High Speed Machine Learning

Machine learning applications are computationally demanding and power intensive. Hardware acceleration of these software tools is a natural step being explored using various technologies. A recurrent processing unit (RPU) is fast and power-efficient hardware for machine learning under development at the University of Maryland. It is comprised of a recurrent neural network and a trainable output vector as a hardware implementation of a reservoir computer. The reservoir is currently realized on both Xilinx 7-series and Ultrascale+ ZYNQ SoCs using an autonomous Boolean network for processing and a Python-based software API. The RPU is capable of classifying up to 40M MNIST images per second with the reservoir consuming under 261mW of power. Using an array of 2048 unclocked gates with roughly 100pS transition times, we achieve about 20 TOPS and 75 TOPS/W.

cs.ET↗