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The growing complexity of computational workloads has amplified the need for efficient and specialized hardware accelerators. Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) have emerged as prominent solutions,…
Coarse-Grained Reconfigurable Arrays (CGRA) are promising edge accelerators due to the outstanding balance in flexibility, performance, and energy efficiency. Classic CGRAs statically map compute operations onto the processing elements (PE)…
Accelerating particles to high energies with a high-gradient wakefield accelerator may require use of multiple stages. Coupling beams from one stage to another can be difficult due to high divergence and non-negligible energy spreads. We…
Federated learning (FL) aims to train machine learning models in the decentralized system consisting of an enormous amount of smart edge devices. Federated averaging (FedAvg), the fundamental algorithm in FL settings, proposes on-device…
We present a variant of the s-step Preconditioned Conjugate Gradient (PCG) method that combines a Chebyshev-stabilized Krylov basis with a Forward Gauss-Seidel (FGS) iteration for the solution of the reduced Gram systems. In s-step…
Critical sized bone defects remain a major clinical challenge, requiring scaffolds that combine mechanical stability with regenerative capacity. Functionally graded (FG) scaffolds, inspired by the graded architecture of native bone, offer a…
Nowadays, there is a desperate need for an ultra-acceleration-gradient method for antimatter particles, which holds great significance in exploring the origin of matter, CP violation, astrophysics, and medical physics. Compared to…
Based on the method of FGD, we apply the method of adaptive gradient descent which uses different step length at different epoch. Adaptive gradient descent performs much better than FGD in the tests and keeps the guarantee of convergence…
Field-programmable gate array (FPGA) based accelerators are being widely used for acceleration of convolutional neural networks (CNNs) due to their potential in improving the performance and reconfigurability for specific application…
Conventional phased-array metasurfaces utilize resonant nanoparticles or nanowaveguides to specify spatially-dependent amplitude and phase responses to light. In nearly all these implementations, subwavelength-scale elements are stitched…
This work proposes an Accelerated Primal-Dual Fixed-Point (APDFP) method that employs Nesterov type acceleration to solve composite problems of the form min f(x) + g(Bx), where g is nonsmooth and B is a linear operator. The APDFP features…
The next generation of high-energy physics experiments requires high intensity protons in the multi-GeV energy range for efficient production of secondary beams. The Fermilab long-term future requires an 8 GeV proton source to feed the Main…
Matrix Factorization is a popular non-convex optimization problem, for which alternating minimization schemes are mostly used. They usually suffer from the major drawback that the solution is biased towards one of the optimization…
This paper presents a comprehensive analysis of the well-known extragradient (EG) method for solving both equations and inclusions. First, we unify and generalize EG for [non]linear equations to a wider class of algorithms, encompassing…
In the general use of a heavy-ion accelerator, an accelerated beam impinged on a target is spoiled into a beam dump. To make more efficient use of the beam, recycling of the beam passed through the target is proposed in the framework of the…
The electron reflection amplitude $R$ at stacking-fault (SF) induced fractional steps is determined for Ag(111) surface states using a low temperature scanning tunneling microscope. Unexpectedly, $R$ remains as high as $0.6 \sim 0.8$ as…
We study the convergence of Nesterov Accelerated Gradient (NAG) minimization algorithmapplied to a class of non convex functions called strongly quasar convex functions. We show thatNAG can achieve an accelerated convergence speed at the…
Asymmetrical Alternate Phase (A-APF) focusing realized in a sequence of 36 Superconducting Quarter Wave Resonators has been shown to accelerate almost 81 % of input Uranium beam before foil stripper to an energy of 6.2 MeV/u from 1.3 MeV/u.…
Riemannian accelerated gradient methods have been well studied for smooth optimization, typically treating geodesically convex and geodesically strongly convex cases separately. However, their extension to nonsmooth problems on manifolds…
The state-of-art seismic imaging techniques treat inversion tasks such as FWI and LSRTM as PDE-constrained optimization problems. Due to the large-scale nature, gradient-based optimization algorithms are preferred in practice to update the…