Techtroniks

 Leading AI systems designs are migrating away from building the fastest AI processor possible, adopting a more balanced approach that involves highly specialized, heterogeneous compute elements, faster data movement, and significantly lower power. Part of this shift revolves around the adoption of chiplets in 2.5D/3.5D packages, which enable greater customization for different workloads and data types, and better performance per watt. Alongside of that, leading-edge chipmakers used the recent Hot Chips 24 conference to show off novel micro-architectures, improvements in pre-fetch and branch prediction, faster memory access, and more intelligent data management on and off chip. These new designs also take aim at NVIDIA’s near monopoly in the AI world due to the proliferation of inexpensive GPUs and the CUDA -based models built atop of them. No general-purpose processor is as energy-efficient as customized accelerators, and rather than a single type of chip, most of the multi-chiple