Abstract: Convolution neural networks (CNNs) have been extensively used in machine learning applications. The most time-consuming part of CNNs are convolution operations. A common approach to ...
Abstract: In this work, we propose a multiplication-less deep convolution neural network, called BD-NET. As far as we know, BD-NET is the first to use binarized depthwise separable convolution block ...
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High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...
A high-performance hardware accelerator designed for image convolution operations in Convolutional Neural Networks (CNNs). This implementation provides an efficient FPGA-based solution for ...
Using the combinatorics of non-crossing partitions, we construct a conditionally free analogue of Voiculescu's S-transform. The result is applied to the analytical description of conditionally free ...
A Calculator for your CPU (Machine Learning Co-processor) Microwatt Momentum 2025 – Proposal Submission Overview: This project proposes a custom co-processor with the open-source Microwatt POWER CPU ...
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