SemiconX:-AI Acceleration Hardware

Artificial Intelligence (AI) is a powerful tool that will be ubiquitous in the upcoming decade, in applications spanning across defense, automobiles, robotics, healthcare, metaverse, and industry 4.0. The increasing AI model capacities demand scaled high-throughput compute at iso-energy consumption which needs a fundamental rethinking of power saving in compute and dataflow. The basic difference of a custom AI hardware architecture w.r.t a general-purpose workload is that deep learning computation and dataflow are structured, and the network is known prior to execution, thus the underlying implementation architecture can be optimized specifically to the AI execution datapath and the hardware overhead for control path can be minimized. Because of the enormous potential in this space, it has gained a lot of traction from investors with several AI hardware startups raising $4B combined and a total valuation of around $10B. The foundational differences in computing architectures can also be classified based on the target applications whether it's for datacenter-scale AI (with both training and high-precision inference workloads) or for edge computing which deploys lightweight models at low-to-intermediate resolution.