Abstract: The paper presents an architecture and an implementation of an FPGA-based sparse matrix-vector multiplier (SMVM) for use in the iterative solution of large, sparse systems of equations ...

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Dynamic Programming (DP) is a powerful technique for solving complex problems by breaking them into smaller, overlapping subproblems and avoiding redundant calculations. It’s a must-know skill for ...

Precision has long been the central bottleneck of analogue computing. Bit-slicing or analogue compensation can be used to perform matrix–vector multiplication with precision, but solving matrix ...

Cyclops is a parallel (distributed-memory) numerical library for multidimensional arrays (tensors) in C++ and Python. Quick documentation links: C++ and Python. Broadly, Cyclops provides tensor ...

Google’s DeepMind research division claims its newest AI agent marks a significant step toward using the technology to tackle big problems in math and science. The system, known as AlphaEvolve, is ...

🚀 Dynamic Programming (DP) is one of the toughest challenges in coding interviews. 💡 But just like any other concept, the quickest way to master it is by recognizing key patterns that simplify ...

Abstract: This paper presents a performance modeling and optimization analysis tool to predict and optimize the performance of sparse matrix-vector multiplication (SpMV) on GPUs. We make the following ...

Room impulse responses (RIRs) between static loudspeaker and microphone locations can be estimated using a number of well-established measurement and inference procedures. While these procedures ...

Over the past decade, Graphics Processing Units (GPUs) have revolutionized high-performance computing, playing pivotal roles in advancing fields like IoT, autonomous ...