Learn ARM Architecture from the Source: A Beginner's Guide

#️⃣ Stuck on Program Execution Diagrams — Need a little help 🤯 I’m working through a classic puzzle in computer architecture: tracing how a simple instruction moves from memory → CPU → PC → IR → AC and back again. You know—the kind of diagram with memory blocks, registers, and arrows everywhere that can make your head spin. 🌀 Here’s what I’ve noticed so far: 🔹 Breaking it down step by step (Step 1, Step 2…) makes the flow easier to follow. 🔹 Visualizing the why behind each move (like why the PC increments, or what IR really stores) makes it click. 🔹 Small, concrete examples > abstract descriptions. But I’d love to hear from you 👇 💡 Got a go-to trick, a mini explainer video, or a problem set that helped you? 💡 Want to join forces in a study group? Also curious: what’s the trickiest part for you? Memory blocks 🗂️ PC/IR flow 🔄 AC interactions ⚙️ #ComputerArchitecture 🖥️ #StudyTips 📚 #LearningTogether 🌱 #Engineering

An impressive resource to understand the architecture and programming of GPUs by Aleksa Gordić. Took me days to read it as it also links to several, high-quality articles. As bloggers and excalidrawist, this blog post is a delight to read as it contains a lot of drawings! The main thing i appreciated is that it makes me very enthusiastic to start GPU programming. The main takeaway is that it makes sense to understand how GPUs work to be able: 1. To program it 2. To optimise compute speed It also covers normal programming as well as virtual instructions. A must-read. I believe we must promote, write and read these kinds of articles!

🧮 𝐍𝐞𝐰 𝐨𝐧 𝐭𝐡𝐞 𝐄𝐬𝐩𝐫𝐞𝐬𝐬𝐢𝐟 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐏𝐨𝐫𝐭𝐚𝐥: 𝐅𝐥𝐨𝐚𝐭𝐢𝐧𝐠-𝐏𝐨𝐢𝐧𝐭 𝐔𝐧𝐢𝐭𝐬 (𝐅𝐏𝐔𝐬) 𝐢𝐧 𝐄𝐬𝐩𝐫𝐞𝐬𝐬𝐢𝐟 𝐒𝐨𝐂𝐬 What is an FPU, why does it matter, and how does it affect real-world performance on Espressif chips? Written in collaboration with Alberto Spagnolo, Senior Developer at WeDo, this article explores how floating-point operations are executed in hardware versus software, and which Espressif SoCs include an FPU. It also shows the performance gains you can achieve through a practical benchmark. 📖 Read the full article here: https://lnkd.in/efUtYbRM

Microsoft presents #BitNet Distillation a lightweight pipeline that fine-tunes off-the-shelf full-precision LLMs (e.g., Qwen) into 1.58-bit precision (i.e., ternary weights {-1, 0, 1}) for specific downstream tasks, achieving strong task-specific performance with minimal computational cost. This tool achieves performance comparable to the full-precision counterpart models across model size, while enabling up to 10x memory savings and 2.65x faster inference on CPUs. Paper and GitHub available on Hugging Face https://lnkd.in/e3apZ_TT

Exploring Low-Level Hardware & CPU Emulation Lately, I’ve been diving deep into low-level computer architecture — trying to truly understand what happens under the hood of modern machines. I recently read But How Do It Know? by J. Clark Scott, and it was an eye-opening experience. The book’s simple yet powerful explanations of how a CPU actually processes instructions inspired me to apply that knowledge — so I decided to build my own CPU emulator. I chose the MOS 6502, the iconic processor used in systems like the NES, Commodore 64, and Apple II. To keep things flexible, I designed it as a modular CPU core, so it can later be reused if I decide to emulate an entire console or computer system. For the implementation: I used C++ for the core emulation logic, focusing on cycle accuracy, instruction decoding, and register handling. I used Python for testing and build automation, which gave me a practical understanding of how build systems streamline real-world development. Writing my own small build and test scripts helped me see why build systems exist — not just to compile code, but to solve genuine problems like dependency management, reproducibility, and testing automation. Through this project, I learned about: Instruction execution and addressing modes Status flags, stack operations, and memory-mapped I/O Cycle-accurate emulation design Testing and verification using thousands of JSON-based test cases This has been a deeply rewarding journey — watching the CPU execute instructions exactly as real hardware would is pure magic ^_^ check out the project here: https://lnkd.in/gwHkRrGf

Building a Lightning-Fast LLM from Scratch? Here’s How It’s Really Done! If you want to deeply understand the difference between “just running a model” and “squeezing every ounce of performance with your bare hands,” Andrew Chan’s deep-dive blog is your blueprint. It’s also brutally honest about where compiler or hardware quirks can destroy your wins, and how hands-on profiling plus a willingness to rewrite simple operations for performance can make or break your project. Andrew covers: - Building LLM inference architecture: attention, feedforward, custom transformer blocks, and KV cache use. - Hardware bottlenecks, focusing on memory bandwidth and the benefits of quantization. - Optimization steps: OpenMP threading, CUDA matrix multiplication, kernel fusion, memory tricks, and loop unrolling for maximum speed. - Benchmark: Achieves 63 tok/s on a RTX 4090, surpassing projects like llama.cpp.

🔧 Ever wondered how Hardware related files looks? Startup File, Linker File In my latest video, I share an overview of two essential files in every ARM Cortex-M4 project — the Startup File, Linker Script. Additionally I have discussed Makefile to make the build handy. These files form the foundation of how your microcontroller boots, organizes memory, and builds your code — yet they’re often overlooked by beginners. 🎥 Check out the video here👉 https://lnkd.in/gnZYx9NP and get a clear understanding of how these files work together before diving deeper! #EmbeddedSystems #CortexM4 #ARM #StartupFile #LinkerScript #Makefile #EmbeddedC #Microcontrollers #stm32 #TechLearning

An Automated RF Testbed with Multi-Hardware Control. Proud to showcase the successful deployment of a remote-controlled RF testing client. The core of this project was a Python application designed to manage a complex hardware stack on a Raspberry Pi. Technical highlights include: ✅ Multi-threaded Architecture: A main thread for command/control, a keep-alive thread for connection stability, and dedicated worker threads for non-blocking SDR tasks using SoapySDR. ✅ Hardware Abstraction: Created dedicated functions to interface with a serial-controlled digital attenuator and manage GPIO states for an amplifier and SPDT switch, ensuring clean and safe hardware sequencing. ✅ Dynamic Configuration: Implemented frequency-specific TX gain and RX attenuation maps, allowing the system to adapt its parameters for optimal performance across different bands (433MHz to 5.8GHz). ✅ System Automation: Deployed the application as a systemd service, featuring pre-start cleanup scripts for GPIO resources, guaranteeing robust, automated operation on boot. This was a rewarding exercise in building a fault-tolerant system for real-time RF testing. #Python #SoapySDR #BladeRF #EmbeddedSystems #Automation #Linux #Systemd #RFengineering #GPIO #EW

If you're working with llama.cpp on Arm hardware, this new Learning Path provides practical insights into profiling inference workloads. The authors walk through how to use Streamline to understand what happens during Prefill versus Decode stages, plus how to evaluate multi-thread execution patterns. The step-by-step approach to performance analysis is exactly what many of us need when working with AI workloads on Arm processors. Worth checking out if this aligns with your work. Great work Zenon (Zhilong) Xiu and Odin Shen 👏

Accelerating GPU Performance GPU computational performance can surpass hybrid CPU/GPU, even for small matrix sizes if host synchronization were eliminated using CUDAGraphs. By default, GPU operations are asynchronous; however, host synchronization forces the CPU (host) to wait for the completion of GPU operations before proceeding. The result is a blocking operation that eliminates the natural asynchronous parallelism between CPU and GPU. Host synchronization impairs performance in GPU computing. Any approach with frequent host sync points (like hybrid CPU/GPU strategies) will severely degrade performance, regardless of how fast individual operators are. By extrapolation, end-to-end pipeline design and CUDAGraph compatibility provide better performance metrics than isolated operator benchmarks. Host synchronization can occur for several reasons including accessing GPU tensor values, moving data to CPU, explicit synchronization calls, and memory operations across devices. These benchmarks showed the dramatic impact of isolated GPU operations: 1.053ms compared to realistic host syncs: 9.867ms (9.4× slower!). The hybrid CPU/GPU approaches are problematic yielding a 14.9% performance overhead from constant device switching that are 1.44× slower than GPU-only pipeline. CUDAGraphs capture the entire computation graph and eliminate per-operation host synchronization. Furthermore, torch.compile (mode="reduce-overhead") automatically reduces host synchronization through: * Batching kernel launches with fewer sync points * Kernel fusion where multiple operations become a single GPU call * Memory pool pre-allocation that eliminates allocation syncs * Graph optimization that minimizes host-device communication Performance improved 2.78× for element-wise operations with torch.compile and reduced the number of host synchronization points. Conclusion: Host synchronization is the hidden performance killer in GPU computing. Any approach that introduces frequent host sync points (like hybrid CPU/GPU strategies) severely degrades performance, irrespective of individual operators speed. Therefore, end-to-end pipeline design and CUDAGraph compatibility are more important than isolated operator benchmarks. GPU computing can outperform hybrid CPU/GPU using CUDAGraphs and torch.compile with reduce-overhead. Source Code References The comprehensive analysis was conducted using these benchmark scripts (now in the pytorch-testing-scripts repository): https://lnkd.in/gjbV3AbJ - Complete benchmarking suite with CUDAGraph and torch.compile support e2e_benchmark_clean.py - End-to-end analysis demonstrating host synchronization impact Repository: https://lnkd.in/gqUuiv2G