This project implements an 8x8 systolic array for high-performance matrix multiplication, leveraging a parallel processing architecture optimized for efficiency and scalability. The workflow spans RTL ...
Abstract: Compute-In-Memory (CiM) is emerging as a promising paradigm to design energy-efficient hardware accelerators for AI, addressing the processor-memory data transfer bottleneck. The popularity ...
Abstract: Numerous studies have proposed hardware architectures to accelerate sparse matrix multiplication, but these approaches often incur substantial area and power overhead, significantly ...
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