Live Coding: Creating memory Buffer for Multi Head Attention Layer using Memory Arena - Part 1

🚀 Live Coding: Building a Memory Buffer for Multi-Head Attention (Memory Arena in Action!) In this live coding session, we dive deep into low-level performance engineering by implementing a custom memory buffer (memory arena) tailored specifically for a Multi-Head Attention layer. If you're interested in systems programming, deep learning internals, or high-performance C/C++, this video will give you a practical look at how memory management can drastically improve efficiency in transformer architectures. 💡 What you'll learn: How Multi-Head Attention uses memory under the hood Designing a custom memory arena allocator Efficient buffer reuse to reduce allocations Improving performance by avoiding heap fragmentation Writing clean, low-level code for ML systems 🧠 This is especially useful if you're: Building your own deep learning framework Optimizing transformer models Exploring how libraries like PyTorch / TensorFlow manage memory internally 🔧 Tech Stack: C Programming | Systems Design | Memory Management | Transformers 👉 https://github.com/umairgillani93/miniTorch 🌐 Connect with me: • GitHub → https://github.com/umairgillani93 • LinkedIn → https://linkedin.com/in/umairgillani93 • Twitter/X → https://x.com/UmairGillani93 🔥 Don’t forget to: 👍 Like the video 💬 Comment your thoughts or questions 🔔 Subscribe for more low-level AI & systems content #CProgramming #LiveCoding #MemoryManagement #Transformers #AIFromScratch #SystemsProgramming #DeepLearning #Tensor #LowLevel #programming 📌 Series Context: This is part of a hands-on series where we: Build attention layers from scratch Optimize memory usage using arenas Debug real-world implementation issues 👍 If you enjoy this kind of deep dive: Like the video Subscribe for more low-level AI + systems content Drop your questions in the comments #️⃣ Tags & Keywords: multi head attention, transformers, memory management, memory buffer, memory arena, attention layer, deep learning, machine learning, c programming, c++ programming, systems programming, low level programming, ai engineering, transformer architecture, attention mechanism, debugging, performance optimization, memory layout, neural networks, ml internals, custom allocator, live coding, coding tutorial, software engineering, backend engineering, ai systems