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Can we accelerate the convergence of gradient descent without changing the algorithm—just by judiciously choosing stepsizes? Surprisingly, we show that the answer is yes. Our proposed Silver Stepsize Schedule optimizes strongly convex functions in \(\(\...\)

Deep reinforcement learning has led to dramatic breakthroughs in the field of artificial intelligence for the past few years. As the amount of rollout experience data and the size of neural networks for deep reinforcement learning have grown continuously, ...

Inference of Large Language Models (LLMs) across computer clusters has become a focal point of research in recent times, with many acceleration techniques taking inspiration from CPU speculative execution. These techniques reduce bottlenecks associated ...

MR data are acquired in the frequency domain, known as k-space. Acquiring high-quality and high-resolution MR images can be time-consuming, posing a significant challenge when multiple sequences providing complementary contrast information are ...$\mathrm{}$

Large Language Models (LLMs) have revolutionized a wide range of applications with their strong human-like understanding and creativity. Due to the continuously growing model size and complexity, LLM training and deployment have shown significant ...

We present a hardware-accelerated SAT solver targeting processor/Field Programmable Gate Arrays (FPGA) SoCs. Our solution accelerates the most expensive subroutine of the Davis-Putnam-Logemann-Loveland (DPLL) algorithm, Boolean Constraint Propagation (...

Ultrasound neurostimulation, a technique that modulates the brain's electrical activity, has emerged as a significant secondary treatment option for cases resistant to pharmacological interventions. The therapy is achievable through the application of a ...

Today, convolutional neural networks (CNNs) are widely used in computer vision applications. However, the trends of higher accuracy and higher resolution generate larger networks. The requirements of computation or I/O are the key bottlenecks. In this ...

The authors formalize the technique of interactive evaluation of the operation efficiency of the motor vehicle system of a large city based on U-statistics methods. To optimize this technique, the authors propose efficient algorithmic ...

Genome Wide Association Study (GWAS) reveals the influence of single nucleotide polymorphisms (SNP) and other genetic markers on the complex genetic disease traits, making a significant contribution to the prevention and treatment of genetic diseases. ...

In order to improve the recognition accuracy of hitting movements, a tennis player hitting movement recognition method based on multimodal data is proposed. First, we collect acceleration modal data of hitting movements and extract acceleration ...

We propose and analyze a new dynamical system with a closed-loop control law in a Hilbert space H, aiming to shed light on the acceleration phenomenon for monotone inclusion problems, which unifies a broad class of optimization, saddle point, and ...

Modern applied optimization problems become more and more complex every day. Due to this fact, distributed algorithms that can speed up the process of solving an optimization problem through parallelization are of great importance. The main ...

We present a simple but effective solution idea to port SIMDified query processing code to Intel® FPGA cards for acceleration. The main advantage of our approach is the seamless integration with existing SIMD abstraction libraries originally developed ...

The densest subgraph problem (DSP) is of great significance due to its wide applications in different domains. Meanwhile, diverse requirements in various applications lead to different density variants for DSP. Unfortunately, existing DSP algorithms ...

Recommender systems are expected to scale to the requirement of the large number of recommendations made to the customers and to keep the latency of recommendations within a stringent limit. Such requirements make architecting a recommender system a ...

Graph-based simultaneous localization and mapping (G-SLAM) is an intuitive SLAM implementation where graphs are used to represent poses, landmarks and sensor measurements when a mobile robot builds a map of the environment and locates itself in it. ...

Verification typically consumes the majority of the time in the hardware development cycle. Primarily this is because multiple iterations to debug hardware using software simulation is extremely time-consuming. While FPGAs can be utilised to accelerate ...

Convolutional neural networks (CNNs) are typically computationally heavy. Fast algorithms such as fast Fourier transforms (FFTs), are promising in significantly reducing computation complexity by replacing convolutions with frequency-domain element-wise ...

The term "metaverse" has become a common word in recent years. This term, a virtual space on the Internet, is gaining importance in various fields in today's era of remarkable progress toward the fusion of virtual space and real space using ...