Abstract: Many of today's deep neural network accelerators, e.g., Google's TPU and NVIDIA's tensor core, are built around accelerating the general matrix multiplication (i.e., GEMM). However, ...

A high-performance hardware accelerator designed for image convolution operations in Convolutional Neural Networks (CNNs). This implementation provides an efficient FPGA-based solution for ...

Abstract: Convolutional neural networks (CNNs) have emerged as one of the most successful machine learning technologies for image and video processing. The most computationally-intensive parts of CNNs ...

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

Enthusiastic and detail-oriented Electronics and Communication Engineering graduate with Honors in VLSI, passionate about digital design and RTL development. Experienced in Verilog-based hardware ...