Bit-Sparsity Aware Acceleration with Compact CSD Code on Generic Matrix Multiplication

The ever-increasing demand for matrix multiplication in artificial intelligence (AI) and generic computing emphasizes the necessity of efficient computing power accommodating both floating-point (FP) and quantized integer (QINT). While state-of-the-art bit-sparsity-aware acceleration techniques have demonstrated impressive performance and efficiency in neural networks through software-driven methods such as pruning and quantization, these approaches are not always feasible in typical generic co…

Source: arXiv cs.AR (Hardware Architecture), published 2026-11-13. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern