Chips that power video games lived on top of the table of priority for long among investors. Lately, the silver of silicon – Artificial Intelligence (AI) chips have taken center stage. This is only part of the ongoing AI revolution. Last Thursday, we witnessed shares of Nvidia grow exponentially to hit 25% with a huge jump in revenue. By Tuesday May 30, 2023, the company had an opportunity taste its position having garnered more than USD1 trillion worth.
Let’s look at AI Chips and Why they are Hitting Headlines
AI chips, also known as AI processors or AI accelerators, are specialized integrated circuits (ICs) designed to perform artificial intelligence (AI) tasks efficiently. Traditional central processing units (CPUs) and graphics processing units (GPUs) are general-purpose chips that are not optimized specifically for AI workloads. In contrast, AI chips are specifically designed to handle the computational demands of AI algorithms, making them more efficient and faster for AI-related tasks.
Artificial Intelligence chips typically have architectures that are specifically tailored for the types of computations required in AI, such as matrix multiplications, vector operations, and neural network calculations. These architectures often include parallel processing units and specialized hardware components, such as tensor processing units (TPUs), which excel at performing the complex mathematical operations involved in neural network training and inference.
Types of AI Chips
There are several types of Artificial Intelligence chips available on the market, each with its own strengths and areas of focus. Some examples include:
1. Graphics Processing Units (GPUs)
Originally designed for rendering graphics in gaming applications, GPUs have found extensive use in AI due to their parallel processing capabilities. They can perform massive amounts of computations simultaneously, making them suitable for training deep learning models.
2. Tensor Processing Units (TPUs)
Developed by Google, TPUs are custom-built ASICs (Application-Specific Integrated Circuits) specifically designed to accelerate AI workloads. TPUs excel at performing matrix operations, which are fundamental to many AI algorithms.
3. Field-Programmable Gate Arrays (FPGAs)
FPGAs are programmable chips that can be customized to perform specific tasks, including AI computations. They offer flexibility and can be reprogrammed for different AI models or algorithms.
4. Application-Specific Integrated Circuits (ASICs)
ASICs are chips designed for specific applications, and there are ASICs specifically developed for AI tasks. These chips are optimized for energy efficiency and performance in AI workloads.
AI chips are commonly used in various AI applications, including computer vision, natural language processing, speech recognition, autonomous vehicles, and robotics. They play a crucial role in accelerating AI tasks, reducing training and inference times, and enabling the deployment of AI models in real-time and resource-constrained environments
Modern Chips and Competition
It has taken 11 years of putting strategies in place that Nvidia has gain its dominant position as a supplier of chips. It’s best product H100 GPU is incomparable to Apple’s latest processor since it packs 80 billion transistors against 13 million in the latter. However, these chips require that you dig deeper into your pocket. For example, H100 GPU has a price tag of USD30,000.
Nonetheless, Nvidia will not have an easy way in the market as other rivals such as Advanced Micro Devices (AMD) are bracing for a face-off with Jesen Huang in an expected race for the appealing market.