The mentioned method is analyzed from different aspects, including its convergence and its ability to accelerate recursive algorithms. We show that this method is capable of improving Iterative Method (IM) as a
We propose fast numerical algorithms to improve the accuracy of singular vectors for a real matrix. Recently, Ogita and Aishima proposed an iterative refinement algorithm for singular value decomposition that is constructed with highly accurate matrix multiplications carried out six times per iteration. ...
However, iterative algorithms may require computations in order to achieve quantitatively accurate results. For the case of baggage scanning, in order to provide fast accurate inspection throughput, they must be accelerated drastically. There are many approaches proposed in the literature to increase ...
Nevertheless, the recent advent of low cost parallel architectures, such as Graphics Processing Units (GPUs), has provided the chance for a remarkable reduction of the image reconstruction time, making iterative methods a realistic alternative to analytic algorithms. Among the different iterative ...
T.: Andersion acceleration for a class of nonsmooth fixed-point problems. SIAM J. Sci. Comput. 43(5), S1–S20 (2021) Article Google Scholar Bollapragada, R., Scieur, D., d’Aspremont, A.: Nonlinear acceleration of momentum and primal-dual algorithms. Math. Program. 198, 325 (2022...
For small systems, solution strategies based on direct methods are typically the preferred choice. However, as the size of the system increases, it becomes necessary to employ iterative approaches in order to efficiently determine the solution. The basic fixed-point techniques that have been ...
Image encoding and decoding (codec in short) are very common and important operations in Internet applications. The Vitis Codec library provides a set of acceleration APIs to accelerate image encoding, decoding and other related algorithms. This tutorial
Typical methods for solving reinforcement learning problems iterate two steps, policy evaluation and policy improvement. This paper proposes algorithms for the policy evaluation to improve learning efficiency. The proposed algorithms are based on the Krylov Subspace Method (KSM), which is a nonstationary...
Therefore, FPGA and ASIC are widely used to accelerate deep learning algorithms. Several papers have addressed the issue of deploying the Transformer on dedicated hardware for acceleration, but there is a lack of comprehensive studies in this area. Therefore, we summarize the transformer model ...
I'm excited about the prospect of super easy-to-use GPU acceleration for the well-known algorithms in the most common Python machine learning libraries. This is going to make a big difference in model accuracy, and it's convenient and not hard to implement. Stay up to date with the lat...