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

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

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

with deep neural networks and tree search." nature 529.7587 (2016): 484-489. 【4】 Wei, Jason, et al. "Chain-of-thought prompting elicits reasoning in large language models." Advances in neural information with large language models." Advances in Neural Information Processing Systems 36 (2024). 【6】Karpas, Ehud, et al. "MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, 【7】Schick, Timo, et al. "Toolformer: Language models can teach themselves to use tools." Advances in Neural Information Processing Systems 36 (2024). 【8】https://huggingface.co/spaces/mteb/leaderboard 【9】https://github

and then use that knowledge to make predictions ◮ Applications: ◮ Computer vision ◮ Medicine ◮ Search engines 6 / 50Embedded devices ◮ Computing devices designed to perform specific tasks within larger paradigm in which an algorithm learns from labeled data to make predictions 11 / 50Neural network (NN) 13 / 50Convolutional neural network (CNN) ◮ Efficient in image classification ◮ A convolutional layer can 50Software dependencies ◮ TensorFlow Lite ◮ OpenCV 16 / 50MobileNet pre-trained model ◮ CNN architecture created by Google ◮ Trained on 1000 classes ◮ Accepts 224x224-pixel images, with 3 color channels

What we do at Snap with C++ 2 Neural style transfer Face tracking Full body tracking Cloth simulation Ray tracing Wrist trackingThank you, Serhii Huralnik and Eduardo Madrid!! 3Section 0. Introduction V ABI Procedure Call Standard for the Arm® 64-bit Architecture armv8-a clang 18.1.0 -O2 -std=c++20 x86-64 (AMD64) System V ABI AMD64 Architecture Processor Supplement x86-64 gcc 14.2 -O2 -std=c++20 System V ABI Procedure Call Standard for the Arm® Architecture armv7-a clang 11.0.1 -O2 -std=c++20 x86 (IA-32) System V ABI Intel386 Architecture Processor Supplement x86-64 gcc 14.2 -O2 -std=c++20

can make. The magic of watching AI do your work for you feels like the early days of email and web search – technologies that fundamentally changed our world. The better / faster / cheaper impacts of 5x Faster vs. Google Note: Dashed-line bars are for years where Google did not disclose annual search volumes. Source: Google public disclosures, OpenAI (12/24). ChatGPT figures are estimates per company 0 2,500 5,000 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Google Search ChatGPT Years Since Public Launch (Google = 9/98, ChatGPT = 11/22)21 In 1998, tapping emerging

Programming Benchmark triSYCL 360k download s 17Sensor Data Training Data Trained Networks Neural Network Training C++ Application Code SYCL in Embedded Systems, Automotive, and AI Compilation and Test Algorithm Today’s Supercomputing Development Workflow needs knowledge of system architecture and tools that control data Optimize Algorithm 2021 Three Pillars of Science Problem 2021

devices, phone, etc, do not have access to GPU. To bridge that gap, micrograd++ let’s any user train a neural network in C++ and ship that to any edge device. II. RELEVANCE The development of micrograd++ is linking process. B. Key Features and Functionalities • Neural Networks: micrograd++ provides comprehensive support for creating and training neural networks. The library includes classes for defining layers propagate gradients through the graph. Layer Class: The Layer class represents a single layer in a neural network, composed of multiple neurons. It supports forward propagation and gradient computation,

with examples. Similarly for most functions or other objects you might encounter! help?> begin search: begin disable_sigint reenable_sigint begin begin...end denotes a block of code. If you already default type for an integer literal depends on whether the target system has a 32-bit architecture or a 64-bit architecture: # 32-bit system: julia> typeof(1) Int32 # 64-bit system: julia> typeof(1) "Hello, world." != "Goodbye, world." true julia> "1 + 2 = 3" == "1 + 2 = $(1 + 2)" true You can search for the index of a particular character using the findfirst and findlast functions: julia> findfirst('o'