```dataviewjs dv.list(dv.pages().where((p) => p.file.path != dv.current().file.path && `"${p.file.folder}"` === `"${dv.current().file.folder}"`).sort((p) => p.file.ctime, 'desc').limit(5).map((e) => "[["+e.file.path+"]]")) ``` ![[Pasted image 20240224081112.png]]![[Pasted image 20240224081123.png]] interesting computing aircraft physics --- title: "Journey Through Parallel Processing: GPUs, FPGAs, and Beyond" date: [Insert Date] categories: [technology, hardware] tags: [#parallel-processing, #gpu, #fpga, #asic, #hardware-design, #simulation, #modeling, #prototyping] --- ## Introduction Embarking on a two-month deep dive into the world of parallel processing, I explored the intricate dance of GPUs, FPGAs, and ASICs. This journey was not just about understanding the hardware but also about harnessing their unique powers for efficient computation. ## Month 1: Laying the Groundwork ### Week 1-2: Parallel Processing Primer I began by unraveling the **basics of parallel processing**, its significance in modern computing, and how GPUs are architected to manage multiple tasks simultaneously. ### Week 3: The FPGA Revelation Next, I ventured into the realm of **Field Programmable Gate Arrays (FPGAs)**, learning their distinction from GPUs and appreciating their flexibility and adaptability. ## Month 2: The Deep Dive ### Week 4-5: Crafting Logic with HDL With a foundation in place, I delved into **Hardware Description Languages (HDL)**, such as VHDL and Verilog, designing and simulating simple logic circuits that would become the building blocks of more complex systems. ### Week 6: Mastering the FPGA Toolkit Setting up an **FPGA development environment** was a rite of passage, familiarizing myself with the tools and workflows that would translate my designs from theory to practice. ## Month 3: Hands-On Experimentation ### Week 7-8: Parallel Algorithms Meet FPGA The real test came when I began **translating parallel algorithms into HDL code**, implementing and simulating them on FPGAs, a process that was both challenging and exhilarating. ### Week 9: Refining the Process Understanding that efficiency is key, I focused on **optimizing my FPGA designs**, tweaking and tuning to squeeze out every bit of performance. ### Week 10: From Virtual to Reality The culmination of my efforts was **FPGA prototyping**, where my virtual designs were brought to life on physical FPGA boards, a tangible testament to the power of hardware design. ## Final Week: Reflection and Projection ### Week 11-12: Documentation and Analysis No journey is complete without reflection. I documented my process, analyzed the performance, and evaluated the efficiency of my FPGA solutions, gaining insights that would inform future projects. ### Week 13: Sharing the Knowledge In the final week, I prepared a presentation to share my findings, not just to showcase my work but to spark conversations and inspire others to explore this fascinating field. ## Conclusion This exploration was more than an academic exercise; it was a transformative experience that expanded my understanding of computational hardware. The blog post you're reading is not just a narrative; it's an invitation to join me on this ongoing journey of discovery. --- If you're intrigued by the potential of parallel processing and want to delve deeper into my journey, stay tuned for upcoming posts where I'll break down complex concepts and share hands-on tips from my personal experience. #journey #learning #hardware #fpga #gpu #asic #technology #innovation Based on the image provided, here's a structured outline of your learning journey: ## Month 1: Foundation and Theory - **Week 1-2:** Focused on **GPU architecture** and **parallel processing** concepts. - **Week 3:** Transitioned to **FPGA basics**, understanding its unique place in hardware design. ## Month 2: Simulation and Modeling - **Week 4:** Began learning **VHDL/Verilog** for HDL. - **Week 5:** Continued with HDL and started designing simple logic circuits. - **Week 6:** Set up **FPGA simulation tools** and familiarized with the environment. ## Month 3: Practical Application and Experimentation - **Week 7:** Implemented **simple parallel algorithms** using HDL. - **Week 8:** Continued with implementation and began simulations. - **Week 9:** Moved onto **optimizing algorithms** for FPGA. - **Week 10:** Conducted **tests on FPGA boards** to bring designs into the real world. ## Final Week: Review and Presentation - **Week 11:** Began compiling **documentation** of the project. - **Week 12:** Finalized documentation and reviewed the entire project. - **Week 13:** Prepared and delivered a **presentation** to share your findings. This timeline provides a clear roadmap of your progression from theoretical understanding to practical application and sharing of knowledge. It's a testament to the iterative process of learning, applying, and refining in the field of hardware design. The image outlines the FPGA design workflow, which is a crucial part of your learning journey. Here's a breakdown of the process: 1. **Design Input**: The initial step where the design specifications are defined. 2. **HDL Input**: Writing the design in a Hardware Description Language (HDL) such as VHDL, Verilog, or System Verilog. 3. **Simulation**: Testing the HDL code using simulators to verify the logic before synthesis. This step can integrate external third-party simulators for enhanced testing capabilities. 4. **Synthesis (Gowin Synthesis)**: The HDL code is translated into a netlist by a synthesis tool, in this case, provided by Gowin. 5. **Place and Route**: The netlist is then used to map the design onto the physical FPGA architecture, considering physical and timing constraints. 6. **Bitstream Generation**: After successful place and route, a bitstream is generated, which is the binary file that will configure the FPGA. 7. **Board Level Validation**: The final step involves loading the bitstream onto an FPGA board and validating the design in a real-world scenario. This workflow is a detailed view of the practical application and experimentation phase of your learning journey, showcasing the transition from theory to real-world implementation. We started by discussing your project on ASIC design for LLMs, focusing on a 3-month timeline where you'd dedicate around 3 hours each Saturday. Initially, the conversation involved reviewing complex diagrams related to ASIC design, semiconductor fabrication, and detailed weekly plans for building an ASIC. You sought advice on creating silicon wafers and photolithography supplies, indicating a focus on the hardware aspect of chip creation. However, upon reflection, you clarified that your interest lies more in understanding GPU architecture and operations rather than the chemistry and physics of chip fabrication. We pivoted to consider how you might use an FPGA to explore concepts like parallel processing, data flow, and power efficiency in a way that aligns more closely with GPU functions. An FPGA can simulate certain GPU aspects and allow you to experiment with hardware design without delving into semiconductor fabrication processes. In response, I provided a new roadmap that aligns with your educational goals, emphasizing learning and simulation over hands-on chemical processes. The new roadmap spans over three months, with a focus on theoretical learning, FPGA-based experimentation, and understanding parallel processing. Lastly, you inquired about purchasing an FPGA development board, the Sipeed Tang Nano 9K, from Amazon. I suggested additional items that might be necessary for a complete setup, ensuring you have all the tools required for your FPGA exploration. Throughout our conversation, I aimed to provide guidance that matches your interests in GPU-like hardware understanding and low-level software interaction, steering away from the initial ASIC fabrication focus. The image depicts a system architecture diagram with various components and their interconnections, alongside a cost breakdown for each component. Here's an interpretation of the diagram and its relevance to your learning journey: - **5V Power Supply Module**: Powers the system, with monitoring and adjustment capabilities. - **ASIC for Neural Network Processing**: The core processing unit for neural network tasks. - **Power Management IC**: Manages power distribution and efficiency for the system. - **DAC/ADC Converter**: Converts between digital and analog signals for processing. - **SPI Memory Interface Controller**: Manages data storage and retrieval operations. - **DDR3 RAM Module**: Provides memory for the system's operations. - **Microcontroller (Arduino Nano)**: Acts as a control unit for user interface and feedback processes. - **GPIO Expansion Board**: Extends the number of general-purpose input/output pins. - **Heat Sink with Fan**: Dissipates heat to maintain optimal operating temperatures. - **External Devices Interface**: Allows for communication with USB, Serial, or Ethernet modules. The cost estimates for each component are provided, which is essential for budgeting and understanding the financial aspect of hardware design. This diagram could represent a stage in your learning journey where you're conceptualizing and planning the hardware aspects of a project, integrating your knowledge of FPGAs, ASICs, and system design. The image provides a detailed overview of the steps involved in ASIC development, from initial design to the final product, as well as the equipment and materials needed for semiconductor fabrication. Here's a summary: ### ASIC Development Process: 1. **Design Concept**: Establish what the ASIC will do and the constraints. 2. **Digital Design**: Use EDA tools for a digital representation of the ASIC. 3. **Design Optimization**: Improve design for performance and cost. 4. **FPGA Prototyping**: Test the design on an FPGA before ASIC fabrication. 5. **Design Iteration**: Refine the design based on FPGA test results. 6. **ASIC Conversion**: Convert the FPGA design to a format for ASIC fabrication. 7. **ASIC Fabrication**: Work with a semiconductor plant for manufacturing. 8. **Testing & Validation**: Ensure each chip functions correctly. 9. **Packaging**: Package the chips for protection and mounting. 10. **Final Product**: The ASIC is ready for system integration. ### Semiconductor Fabrication Equipment and Materials: 1. **Silicon Wafers**: The base material for chip creation. 2. **Lithography Equipment**: For transferring circuit designs onto wafers. 3. **Photomasks**: Custom masks for circuit patterns. 4. **Chemicals for Etching and Developing**: Materials for creating the circuit patterns on the wafer. 5. **Doping Chemicals**: For creating n-type or p-type regions in the silicon. 6. **Cleanroom Supplies**: Essentials for a contamination-free environment. 7. **Safety Equipment**: Protective gear for safe handling of materials. 8. **Furnace or Oven**: For annealing and activating dopants. 9. **Microscope**: For wafer and mask inspection. 10. **Vacuum Chamber**: For metal deposition. 11. **Testing and Measurement Equipment**: Tools for evaluating the semiconductor's properties. 12. **Computers and Software**: For circuit design and lithography control. This information is integral to understanding the complexities and resources involved in ASIC development, which is a step beyond FPGA prototyping and involves a significant investment in time, expertise, and capital. The image presents a conceptual map detailing the hardware requirements for ASIC development tailored to the needs of Large Language Models (LLMs). Here's a summary of the key points: - **LLM Architecture**: Recognizes the limitations of current CPU/GPU setups in terms of performance and power efficiency when dealing with LLMs. - **LLM Hardware Needs**: Outlines the specific hardware capabilities required to effectively run LLMs: - **High Processing Power**: For handling complex neural network calculations. - **Efficient Memory Usage**: To store and access large parameter sets. - **Parallel Processing Capability**: For matrix operations and simultaneous tasks. - **Optimized Data Transfer**: For rapid input/output of large text sequences. - **Energy Efficiency**: To manage high computational loads with minimal power waste. - **Scalability**: To accommodate growing model sizes and computational demands. - **Specialized Computational Units**: For performing specific LLM operations. - **Advanced Cooling Mechanisms**: To dissipate heat from intensive operations. This map serves as a guide for understanding the specialized requirements of LLMs and could inform the design and development of ASICs that are optimized for such tasks. It emphasizes the need for hardware that goes beyond traditional computing paradigms to support the advanced capabilities of LLMs. Your goals for this project are centered around gaining a deeper understanding of the following aspects: 1. **GPU Structure/Architecture**: You want to learn the exact structure and architecture of a GPU, including its core components and how they are organized. 2. **Data Movement**: You aim to understand how information moves within a GPU, including data transfers from memory to the compute units. 3. **Algorithmic Efficiency**: You are interested in how hardware can be optimized to perform specific algorithms effectively, and what makes certain hardware better suited to particular computational tasks. 4. **Energy/Power Implications**: You seek to understand the energy consumption and power requirements of GPUs, and how these factors are managed. 5. **Mechanical Parts**: You wish to explore the mechanical aspects of GPU hardware, possibly including the physical build and cooling mechanisms. 6. **Low-level Software Components**: You aim to delve into the software that interacts directly with the GPU hardware, such as drivers and firmware, to gain an understanding of low-level software optimization. 7. **Educational Purpose**: Overall, your project has an educational focus, aiming to demystify complex hardware concepts in a way that can be taught and understood clearly. Throughout the conversation, it became apparent that the initial path of ASIC fabrication wasn't aligned with these goals, leading to a pivot towards using FPGAs to simulate GPU operations. The FPGA platform would allow for experimentation with parallel processing and hardware design, providing a practical and less chemically involved approach to understanding these concepts. ## drafts ### 250224 What is a Computer? A computer is composed of multiple components interacting with each other, resulting in what you use every day: iPhone, Laptop, Smartwatch, your coffee machine, etc. ![[Pasted image 20240225101524.png]] The "RAM" (Random Memory Access) is like a drawer, it stores things. It's like the computer short-term memory. The "CPU" (Core Processing Unit) is like a human moving objects between drawer cabinets. The disk is another form of memory, but rather for long-term memories. The "GPU" (Graphics Processing Unit) does the same thing than the CPU but thing of it at if it's rather plenty of small birds that can take smaller objects "in parallel" and moving to the other cabinets. GPUs are highly efficient for parallel processing, originally created for graphics that tend to involve many parallel operations, but it has been discovered that they are also ideal for the repetitive, data-intensive tasks required in AI and deep learning. ![[Pasted image 20240225102336.png]] ![[Pasted image 20240224093822.png]] ### old It's not hard not to use AI for writing nowadays. But writing is the best way to think, so if you're not able to write without AI, you're not able to think anymore You became like a passenger in Google Maps algorithm, a cog in the wheel GOAL: - develop my understanding of the computer - develop my understanding of ai accelerators - share something useful - Who is your target audience? blog post 1 - how computer work blog post 2 - how gpu work blog post 3 - how fpga work blog post 4 - how asic work blog post 5 - neuromorphic AI Computing Hardware: A Retrospective and Prospective Analysis Table of Contents 1. Introduction Setting the Stage: The Evolution of AI Hardware Blog Post Goals and Structure 2. The Early Days What is a Computer? Birth of AI Accelerators: Concept to Reality Evolution of Architecture for AI 3. Exploring AI Hardware GPUs: The Backbone of AI Computing FPGAs: Experimenting with Hardware Design ASICs: New architectures 4. The Present Scenario Analyzing Current AI Accelerators Real-World Applications and Impact 5. Visions of the Future Innovations on the Horizon: What's Next for AI Hardware? Preparing for the Next Wave of AI Accelerators 6. Conclusion Lessons Learned and Looking Forward Encouraging Exploration and Innovation in AI Hardware 7. Resources Recommended Readings Tools and Technologies for AI Hardware Exploration The Early Days What is a Computer? The hardware that powers AI, particularly GPUs and CPUs, is a critical component of the technological ecosystem. To appreciate the current hardware landscape and its challenges, it's essential to understand how computers work at a fundamental level. The Basics of Computer Operation At its core, a computer operates through a cycle of fetching, decoding, and executing instructions. These instructions are processed by the Central Processing Unit (CPU). The CPU performs arithmetic and logic operations and makes decisions based on the input data. Parallel to the CPU, the Graphics Processing Unit (GPU) is designed to render images and handle complex mathematical calculations. GPUs are highly efficient at parallel processing, making them ideal for the repetitive and data-intensive tasks required in AI and deep learning. Imagine a computer as a librarian rapidly retrieving and shelving books according to queries. ![[Pasted image 20240222115623.png]] ![[Pasted image 20240224093822.png]] Birth of AI Accelerators: Concept to Reality They are also not purely hardware-driven, and in fact — much of the AI accelerator industry’s focus has been around building robust and sophisticated software libraries and compiler toolchains Evolution of Architecture for AI The Problem with Current Hardware Despite their capabilities, current hardware systems face several issues: - Energy Inefficiency: The frequent data transfers between CPU, GPU, and memory result in a bottleneck, causing significant energy inefficiency. This inefficiency stems from the necessity of moving data for each operation, which is both time-consuming and power-intensive. Specifically, Nvidia GPUs exhibit this inefficiency due to their internal data shuttling. - Fragility and Redundancy: In contrast to the natural world, where organisms have evolved with redundant systems (like the lungs) to enhance resilience and survivability, our computer systems are inherently fragile, lacking the necessary redundancy for operation in harsh or extraterrestrial environments. - Cooling Systems: High-performance computing generates a lot of heat. Current cooling systems are often bulky and energy-intensive, which is not ideal for scaling or for environments where cooling is challenging. - Maintenance Costs: The complexity of these systems leads to high repair and maintenance costs. Predictive and preventive maintenance is required to ensure uptime, adding to the total cost of ownership. The graphic below illustrates the competitive landscape of the hardware market, highlighting key players and their market capitalization. It's a dynamic field with a mix of established giants and nimble innovators, all contributing to the evolution of AI hardware. ![[Pasted image 20240222105823.png]] As we continue to push the boundaries of what's possible, the synergy between hardware advancements and software breakthroughs will be crucial. The future of AI depends not just on the algorithms and models we develop, but also on the physical infrastructure that supports them. By drawing inspiration from nature and leveraging cutting-edge technology, we are on the cusp of a new era in computing that will redefine our relationship with technology. ![[Pasted image 20240222105806.png]] In conclusion, the journey towards more efficient, resilient, and intelligent hardware is not just a technical challenge; it's a necessary step in our quest to harness the full potential of AI. As we look to the future, it's clear that the innovations we make today will lay the groundwork for the AI-driven world of tomorrow.