Leveraging the power of C++ for efficient machine learning on embedded devices Adrian Stanciu adrian.stanciu.pub@gmail.com CppCon, 2023 1 / 50About me ◮ I am a software engineer from Romania ◮ I have Image classification ◮ Hand gesture recognition ◮ Summary ◮ Q&A 4 / 50Motivation 5 / 50Machine Learning (ML) ◮ Subfield of Artificial Inteligence (AI) ◮ Enables computers to learn from data and then consumption ◮ May have real-time performance constraints 7 / 50Machine learning on embedded devices ◮ Alternative to cloud-based machine learning ◮ Advantages: ◮ Real-time processing ◮ Low latency ◮ Reduced bandwidth

micrograd++: A 500 line C++ Machine Learning Library Gautam Sharma Independent Researcher gautamsharma2813@gmail.com Abstract—micrograd++ is a pure C++ machine learning li- brary inspired by Andrej Karpathy’s for building and training machine learning models. By leveraging the performance efficiency of C++, micro- grad++ offers a robust solution for integrating machine learning capabilities directly into C++-based Traditionally, all machine learning libraries are extremely bulky and very hard to integrate as third party dependencies. This aspect scares practitioners to adopt a C++ based machine learning library for prototyping

Intelligence,’ a term he coined 1/62: Arthur Samuel, an IBM computer scientist, creates a self-learning program that proves capable of defeating a top USA checkers champion AI ‘Winter1’ (1967-1996) Shakey, the first general- purpose mobile robot that can reason about its own actions 5/97: Deep Blue, IBM’s chess- playing computer, defeats Garry Kasparov, the world chess champion Trending = Unprecedented37 Machine-Learning Model* Trending = In 2015... Industry Surpassed Academia as Data + Compute + Financial Needs Rose *Machine Learning = A subset of AI where machines learn

TVM: Where are we going Tianqi ChenCurrent Deep Learning Landscape Frameworks and Inference engines DL Compilers Kenrel Libraries Hardware CuDNN NNPack MKL-DNN Hand optimized Open source, automated automated end-to- end optimization framework for deep learning.TVM Stack High-Level Differentiable IR Tensor Expression and Optimization Search Space LLVM, CUDA, Metal VTA Edge FPGA Cloud FPGA FPGA ASIC Optimization AutoTVM Device FleetExisting Deep Learning Frameworks High-level data flow graph Hardware Primitive Tensor operators such as Conv2D eg. cuDNN Offload to heavily optimized

the model uses to predict a specific output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. However, crafting the most effective prompt can be complicated model’s ability to provide meaningful output. You don’t need to be a data scientist or a machine learning engineer – everyone can write a prompt. Prompt Engineering February 2025 7 When you chat with temperature control can be understood in a similar way to the softmax function used in machine learning. A low temperature setting mirrors a low softmax temperature (T), emphasizing a single, preferred

helps us be more efficient and creative. Elena Alfaro Head of Global AI Adoption Product Note: With deep research, ChatGPT can do work independently. Give it a prompt, and it can synthesize hundreds of employee productivity and gives them access to deep, detailed research on any topic in minutes. In an internal evaluation by experts across domains, deep research saved an average of 4 hours per complex accepting inefficient processes as a cost of doing business. 21 AI in the EnterpriseConclusion Learning from each other As the previous examples show, every business is full of opportunities to harness

but have never had a chance to study design patterns and need some resources to help orient them. Learning about design patterns and where to apply them can at the least give you a way to think about how for object-oriented programming 26 ● I really enjoyed this book (as a graphics programmer) for learning design patterns. ○ There’s a free web version here: https://gameprogrammingpatterns.com/ ○ I rendering involves: ■ loading font files, a custom string class, a drawable object class, and a deep hierarchy... ■ Oh, by the way, the text must always be green and uppercase letters.Problem we are

Transactions on Information Theory, 2(3), 61-79. 【3】Silver, David, et al. "Mastering the game of Go with deep neural networks and tree search." nature 529.7587 (2016): 484-489. 【4】 Wei, Jason, et al. "Chain-of-thought Processing Systems 36 (2024). 【8】https://huggingface.co/spaces/mteb/leaderboard 【9】https://github.com/deep-floyd/IF 【10】https://developer.nvidia.com/blog/pushing-the-boundaries-of-speech-recognition-with-nemo-parakeet-asr- 在 IntelliJ IDEA 中，我们可以看到 AI 功能的加入，如：原生的向量化模型、基于语义化搜 索（SearchEverywhere）、结合补全统计的机器学习补全插件 Machine Learning Code Completion、适用于单个代码行的 Full Line Code Completion 等等。 而除了 GitHub Copilot 工具本身，它还开放了其插件能力，使得我们可以定义自己的

Canada ● Chair of Programming Languages for Standards Council of Canada Chair of WG21 SG19 Machine Learning Chair of WG21 SG14 Games Dev/Low Latency/Financial Trading/Embedded ● Editor: C++ SG5 Transactional Critical Advisory Forum • OpenCL/SYCL Safety Critical • Vulkan Safety Critical • JTC1/SC42 Machine Learning WG3 Trustworthiness • ITC22/SC32 SOTIF WG8 SOTIF, WG13, WG14 • SAE ORAD • UL4600 • RISC-V Safety/Security which is much harder in multithread system • Heterogeneous-> AI/ML safety Stage 1: extensive deep analysis of 81 rules • Started in 2019 at a MISRA meeting • Why are there no rules for parallelism

comments about this tutorial please comment below. Alternatively, if you are having any issues learning about a specific portion of Unity3D, comment below and we can make a tutorial on it as soon as Page 76 Fundamentals of 3D Development with Unity3D By Jesse Glover Today, we will start the deep dive into what most programmers see as the holy grail of game development, 3D games. Unity3D makes Page 126 TUTORIAL SCREENCAST For the ones with a more visual learning style, I’ve prepared a screencast showing what we do in this written article Here LET’S GET STARTED