Published On: January 2023

Photo by Jelleke Vanooteghem on Unsplash The use cases of Artificial Intelligence are increasing year after year. To deliver the much-required performance, the silicon technology platform is critical. The computing industry has developed different types of AI-inspired applications and is now looking for a perfect silicon architecture that can cater to different application scenarios. In this process, the semiconductor industry has provided the silicon platforms like GPUs, TPUs, NPUs, and AIUs. The common trait across these silicon platforms has been the internals on how different data processing occurs. Eventually, the AI applications require high throughput to ensure the time taken to perform inference and training is the smallest possible. Memory Management: AI Semiconductor Stack Demands Speedy And Error-Free Data Movement Via Memory Stacks. Data Storage: Enabling On-The-Go Analysis Requires Silicon

Photo by DeepMind on Unsplash System-On-A-Chip (SoC) has been in the market for decades. The core-level features have provided the much-needed processing capabilities to drive data-driven applications. However, the capabilities provided by the underlying architecture of SoC are not suitable for applications that are always crunching and training the data. For such applications, a faster processing capability is a must. Which the traditional SoCs are not capable of providing. Speed: AIUs Are Very Good At Throughput-Oriented Tasks. Training: Faster Processing Enables Speedy Training Of Big Data Set. It is where a new set of computer architecture comes into play. These are Artificial Intelligence Units (AIUs), which cater to the training demand of new-age applications. Given the purpose of AIUs is data throughput, the inference and training part of the data