Really interesting article for someone not in this field of semiconductor products. > Put simply, we have entered the age of dedicated processing architectures

Really interesting article for someone not in this field of semiconductor products. > Put simply, we have entered the age of dedicated processing architectures. “AI is asking for more than Moore’s Law can deliver,” says Tim Kogel, principal applications engineer at Synopsys. “The only way forward is to innovate the computer architecture by tailoring hardware resources for compute, storage, and communication to the specific needs of the target AI application. We see the same trend in other domains, where the processing and communication requirements outpace the evolution of general-purpose compute, such as in the data center or 5G base stations. In data centers, a new class of processing unit for infrastructure and data-processing tasks has emerged. These are optimized for housekeeping and communication tasks, which otherwise consume a significant portion of the CPU cycles. Also, the hardware of extreme low-power IoT devices is tailored for the software to reduce overhead power and maximize computational efficiency.” And practical applications like running AI on edge devices: > AI applications on the edge often are driven by power more than anything else. “Customers want to run more and more AI, but they need to run it more efficiently,” says Paul Karazuba, vice president of marketing for Expedera. “This is especially true when you move away from the data center, and toward an edge node, where an edge node can be defined as anything from a smart light bulb all the way up to a car. There is a need for specialized AI acceleration because there is a need for power efficiency, and there is a need for performance efficiency. Just running the same engines is not powerful enough. You will start to see a lot of different architectures in chips, mostly because they can be much more efficient than others.”