“Several manufacturers have already started to commercialize near-bank Processing-In-Memory (PIM) architectures. Near-bank PIM architectures place simple cores close to DRAM banks and can yield ...
Sparse matrix computations are pivotal to advancing high-performance scientific applications, particularly as modern numerical simulations and data analyses demand efficient management of large, ...
Optical computing uses photons instead of electrons to perform computations, which can significantly increase the speed and energy efficiency of computations by overcoming the inherent limitations of ...
A novel AI-acceleration paper presents a method to optimize sparse matrix multiplication for machine learning models, particularly focusing on structured sparsity. Structured sparsity involves a ...
Abstract: The Multiply and Accumulator (MAC) in Convolution Neural Network (CNN) for image applications demands an efficient matrix multiplier. This study presents an area- and power-efficient ...
Abstract: The sparse matrix-vector (SpMV) multiplication routine is an important building block used in many iterative algorithms for solving scientific and engineering problems. One of the main ...
MatrixForge is a specialized hardware coprocessor implemented in VHDL that performs matrix multiplication on two 3×3 matrices. The system calculates the element-wise product of corresponding matrix ...
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