High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...

parallel_processing_project/ ├── matrix/ │ └── Matrix.java # Matrix data structure ├── algorithms/ │ ├── MatrixMultiplier.java # Interface for multipliers │ ├── SequentialMultiplier.java # Sequential ...

Abstract: Stochastic circuits offer the benefits of small area and lower power consumption. However, as the bit width of the operands increases, the area and latency of stochastic circuits also need ...

Multiplying the content of two x-y matrices together for screen rendering and AI processing. Matrix multiplication provides a series of fast multiply and add operations in parallel, and it is built ...

Introduction to parallel computing for scientists and engineers. Shared memory parallel architectures and programming, distributed memory, message-passing data-parallel architectures, and programming.

As Transformer models continue to grow in size and complexity, numerous high-fidelity pruning methods have been proposed to mitigate the increasing parameter count. However, transforming these ...

Abstract: Emerging applications, e.g., machine learning, large language models (LLMs), and graphic processing, are rapidly developing and are both compute-intensive and memory-intensive. Computing in ...