arXiv subjects

Nick Moore

Publications and source records attributed to Nick Moore.

4 recordsLinked to original sources

arXiv2023

Photonic Accelerators for Image Segmentation in Autonomous Driving and Defect Detection

Photonic computing promises faster and more energy-efficient deep neural network (DNN) inference than traditional digital hardware. Advances in photonic computing can have profound impacts on applications such as autonomous driving and defect detection that depend on fast, accurate and energy efficient execution of image segmentation models. In this paper, we investigate image segmentation on photonic accelerators to explore: a) the types of image segmentation DNN architectures that are best suited for photonic accelerators, and b) the throughput and energy efficiency of executing the different image segmentation models on photonic accelerators, along with the trade-offs involved therein. Specifically, we demonstrate that certain segmentation models exhibit negligible loss in accuracy (compared to digital float32 models) when executed on photonic accelerators, and explore the empirical reasoning for their robustness. We also discuss techniques for recovering accuracy in the case of models that do not perform well. Further, we compare throughput (inferences-per-second) and energy consumption estimates for different image segmentation workloads on photonic accelerators. We discuss the challenges and potential optimizations that can help improve the application of photonic accelerators to such computer vision tasks.

cs.CV↗

arXiv2023

Parameter Estimation for the Truncated KdV Model through a Direct Filter Method

In this work, we develop a computational method that to provide realtime detection for water bottom topography based on observations on surface measurements, and we design an inverse problem to achieve this task. The forward model that we use to describe the feature of water surface is the truncated KdV equation, and we formulate the inversion mechanism as an online parameter estimation problem, which is solved by a direct filter method. Numerical experiments are carried out to show that our method can effectively detect abrupt changes of water depth.

math.NA↗

arXiv2021

An Electro-Photonic System for Accelerating Deep Neural Networks

The number of parameters in deep neural networks (DNNs) is scaling at about 5$\times$ the rate of Moore's Law. To sustain this growth, photonic computing is a promising avenue, as it enables higher throughput in dominant general matrix-matrix multiplication (GEMM) operations in DNNs than their electrical counterpart. However, purely photonic systems face several challenges including lack of photonic memory and accumulation of noise. In this paper, we present an electro-photonic accelerator, ADEPT, which leverages a photonic computing unit for performing GEMM operations, a vectorized digital electronic ASIC for performing non-GEMM operations, and SRAM arrays for storing DNN parameters and activations. In contrast to prior works in photonic DNN accelerators, we adopt a system-level perspective and show that the gains while large are tempered relative to prior expectations. Our goal is to encourage architects to explore photonic technology in a more pragmatic way considering the system as a whole to understand its general applicability in accelerating today's DNNs. Our evaluation shows that ADEPT can provide, on average, 5.73$\times$ higher throughput per Watt compared to the traditional systolic arrays (SAs) in a full-system, and at least 6.8$\times$ and $2.5\times$ better throughput per Watt, compared to state-of-the-art electronic and photonic accelerators, respectively.

cs.AR↗

arXiv2016

Enhanced spin lifetimes in a two dimensional electron gas in a gate-controlled GaAs quantum well

Exciton, trion and electron spin dynamics in a 20 nm wide modulation-doped GaAs single quantum well are investigated using resonant ultrafast two-color Kerr rotation spectroscopy. Excitons and trions are selectively detected by resonant probe pulses while their relative spectral weight is controlled by adjusting the gate voltage which tunes the carrier density. Tuning the carrier density markedly influences the spin decay time of the two dimensional electron gas. The spin decay time can be enhanced by a factor of 3 at an intermediate carrier concentration in the quantum well, where excitons and trions coexist in the system. In addition, we explore the capability to tune the g-factor of the electron gas via the carrier density.

cond-mat.mes-hall↗