IEEE

A Stochastic Rounding-Enabled Low-Precision Floating-Point MAC for DNN Training

Abstract: Training Deep Neural Networks (DNNs) can be computationally demanding, particularly when dealing with large models. Recent work has aimed to mitigate this computational challenge by ...

Analytics Jobs Unveils Comprehensive Simplilearn Reviews: Empowering Learners in Analytics Jobs Era

Analytics Jobs, as India’s leading course reviews portal, emphasizes transparency: while Simplilearn excels in faculty (5/5) and support, minor platform glitches like slow labs are noted, yet ...
GitHub

point-of-sale

Open Source Point of Sale is a web based point of sale application written in PHP using CodeIgniter framework. It uses MySQL as the data back end and has a Bootstrap 3 based user interface. The base ...
IEEE

Unsupervised Point Cloud Pre-training via Occlusion Completion

Abstract: We describe a simple pre-training approach for point clouds. It works in three steps: 1. Mask all points occluded in a camera view; 2. Learn an encoder-decoder model to reconstruct the ...
lablab

Smart Robot Monitor with AI Alerts

To keep this tutorial practical, we'll simulate robot telemetry data using Python rather than connecting to actual physical robots. This simulator mimics real-world robot behavior and continuously ...
IEEE

Accelerating Training Convergence for Point Cloud Semantic Segmentation of Large-Scale Urban Scenes with Scene-Ensemble Prototypes

Abstract: Point cloud semantic segmentation serves as a vital means of remote sensing, with existing segmentation networks capable of achieving commendable results. However, the complex network ...
GitHub

Voxel-MAE: Masked Autoencoders for Pre-training Large-scale Point Clouds

Mask-based pre-training has achieved great success for self-supervised learning in images and languages without manually annotated supervision. However, it has not yet been studied for large-scale ...
IEEE

A 28-nm 8-bit Floating-Point Tensor Core-Based Programmable CNN Training Processor With Dynamic Structured Sparsity

Abstract: Training deep/convolutional neural networks (DNNs/CNNs) requires a large amount of memory and iterative computation, which necessitates speedup and energy reduction, especially for edge ...
IEEE

A 28nm 8-bit Floating-Point Tensor Core based CNN Training Processor with Dynamic Activation/Weight Sparsification

Abstract: We present an 8-bit floating-point (FP8) training processor which implements (1) highly parallel tensor cores (fused multiply-add trees) that maintain high utilization throughout forward ...
IEEE

Embedding Spatial Augmented Reality in Culinary Training: A Comparative Evaluation of sAR Kitchen and Video Tutorials

Abstract: Cooking is a multitasking and rule-based task that can benefit from augmented reality (AR). This article introduces sAR Kitchen, an AR-based cooking assistant designed to incorporate spatial ...
IEEE

Contrastive Pre-Training with Multi-View Fusion for No-Reference Point Cloud Quality Assessment

Abstract: No-reference point cloud quality assessment (NR-PCQA) aims to automatically evaluate the perceptual quality of distorted point clouds without available reference, which have achieved ...