Rodrigo LABS

As I explored the field of artificial intelligence and machine learning, I realized how important hardware is for training and deploying models. During my research, I learned that GPUs and TPUs have evolved in very different ways and serve different purposes. From what I observed, GPUs were not originally designed for AI. They were created for graphics rendering and gaming by companies such as NVIDIA and AMD. However, researchers later discovered that GPUs could perform thousands of calculations simultaneously, making them ideal for scientific computing and deep learning. This discovery transformed GPUs from gaming hardware into the backbone of modern AI development. Today, most machine learning tutorials, frameworks, and research projects are built around GPU acceleration. I also came across TPUs, which are Tensor Processing Units developed by Google. Unlike GPUs, TPUs were designed specifically for machine learning. Google introduced the first TPU in 2016...

Making a CPU, which is the brain of a computer, is a very complex process. It starts with the design. Designers decide how the CPU will work, including how many cores it has and how fast it can process information. Before building it, they use computer programs to test the design and make sure it works correctly. We mainly use silicon as the raw material for the production process. Silicon is a material that comes from sand and can carry electricity when treated in certain ways, as it is a semi conductor . Now  lets dive into the production process of the microchip➡️ 🥇The silicon is purified and shaped into a large cylinder called an ingot . This cylinder is then sliced into very thin discs called wafers, which will become the base for all the CPU circuits. 🥈After that comes photolithography . This is a process where the CPU’s design is printed onto the wafer using light. A special chemical called photoresist is applied to the wafer, which reacts to light. The light makes certain...