Outline ● What is plugin architecture? ● Why use plugin architecture? ● Designing a simplified plugin architecture ● Library used in robotics to implement plugin based system ○ Pluginlib ● Case study study for plugin architecture - MoveIt ● Limitations ● Summary 2Introduction •Abi Sivaraman •Robotics Engineer at PickNik Robotics •I work with robotic arms •MoveIt Maintainer 3What is plugin plugin architecture? Software Design Pattern that allows for developers to add functionality to a larger system without having to alter the source code of the system itself. Plug-ins are self-contained

API server-side architecture for Beginners GopherCon ���� ����.��.�� - @hgsgtk © ����-���� BASE, Inc. � Talk abstract • A practical approach to build server-side architecture in a Go project � Problem of building architecture for beginners � Approach to build architecture � Summary � Talk structure © ����-���� BASE, Inc. � Problem of building architecture for beginners � Approach Approach to build architecture � Summary � Talk structure © ����-���� BASE, Inc. � Why I need server-side architecture �.Keep a design easy to change • -> Separate external input/output and business

Unified Data Layers: A New Era for Scalable Analytics, Search, and AI v 1.1Table of Contents Introduction 1. The Interconnection of Analytics, Search, and AI 2. What is a Real-Time Unified Data Layer unprecedented volumes of data across a growing number of sources and formats, data engineering and architecture teams must design systems that not only scale but also deliver real-time access and insights. personalize experiences and ensure performance. 32. The Interconnection of Analytics, Search, and AI Analytics, search, and AI are deeply interconnected in how they process, interpret, and extract value

uptake in many different computing sectors. The book also contains many insights about computer architecture in general, as well as explaining the particular de- sign choices we made in creating RISC-V. the point, and complete. The book’s commentaries provide a gratuitous history, motivation, and architecture critique. —C. Gordon Bell, Microsoft and designer of the Digital PDP-11 and VAX-11 instruction handy little book effortlessly summarizes all the essential elements of the RISC-V Instruction Set Architecture, a perfect reference guide for students and practitioners alike. —Professor Randy Katz, University

embedded software • Started using C++ in 1995 • First cross-platform project in 1994Cross-Platform Architecture Goals • Take advantage of all platforms • Focus on the compiler • Minimize boilerplate and unnecessary requiring implementations that differ depending upon the target machine architecture. • Features may be hardware: CPU architecture, SIMD instruction set, DMA controller, GPIO module, etc. • Features

plethora of choices that we face when training a deep learning model in the computer vision domain. A Search Space for n parameters is a n-dimensional region such that a point in such a region is a set of each of those parameters. The parameters can take discrete or continuous values. It is called a "search" space because we are searching for a point in which minimizes (or maximizes) an Evaluation Function example for choosing quantization and/or clustering techniques for model optimization. We have a search space which has two boolean valued parameters: quantization and clustering. A $$True$$ value means

problems. Machine learning in turn is one approach towards artificial intelligence. Deep learning with neural networks has been the dominant methodology of training new machine learning models for the past decade Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems 25 (2012): 1097-1105. do linear algebra operations the ImageNet dataset. 2 Glorot, Xavier, Antoine Bordes, and Yoshua Bengio. "Deep sparse rectifier neural networks." Proceedings of the fourteenth international conference on artificial intelligence and

shuffle_weights(bert_classifier) return bert_classifier Let’s invoke the training with the BERT-Small model architecture, but not its weights (we will set the keep_tfhub_weights parameter to False). bert_small_fro Using a pre-trained BERT-Base model achieves a best accuracy of 93.97%, while using the same architecture but not the pre-trained model achieves a best accuracy of 90.07%. Refer to figure 6-9. Figure directly optimize for similarity between and , but the authors found that it was better to add a small neural network referred to as the ‘projection head’ (represented by the function ) to first project the

import text_to_word_sequence # NLTK Import try: from nltk.corpus import wordnet # Placeholder search to ensure wordnet data is available. wordnet.synsets('hello') except LookupError as e: import implements random shuffling: # NLTK Import try: from nltk.tokenize import sent_tokenize # Placeholder search to ensure wordnet data is available. sent_tokenize('hello') except LookupError as e: import nltk these transformations is that they are intuitive and can be applied without changes to the model architecture. Their benefit is clear in the low data situations as demonstrated through the projects. In the

and Cython. Automatic differentiation is done with a tape-based system at both a functional and neural network layer level. This functionality brings a high level of flexibility and speed as a deep learning framework and provides accelerated NumPy-like functionality. PyTorch also includes standard defined neural network layers, deep learning optimizers, data loading utilities, and multi-gpu, and multi-node support corresponds to GPUs in the NVIDIA Pascal, NVIDIA Volta™, NVIDIA Turing™, NVIDIA Ampere architecture, and NVIDIA Hopper™ architecture families. For a list of GPUs to which this compute capability corresponds, see