You’ve checked for understanding—now you can use this framework to understand what students’ confusion is telling you, and how you can adjust course.

Abstract: Numerous studies have proposed hardware architectures to accelerate sparse matrix multiplication, but these approaches often incur substantial area and power overhead, significantly ...

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 ...

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 ...

Welcome to NumPyLiteC, a C implementation inspired by NumPy, designed to provide fundamental array operations and utilities. This project aims to offer a lightweight, efficient library for handling ...