CPUs vs GPUs: Which chips will give firms the AI edge?
Mumbai: Early this month at the Intel AI Devcon 2018 in Bengaluru, a holographic avatar called Ella listened intently to composer Kevin Doucette playing notes on his synthesizer. When he paused, she began composing her own notes, complementing his music in real-time.
How did this happen? Ella was learning about features such as tempo, scale and pitch from the music data that was being sent in real-time to an Intel Movidius Neural Compute Stick. Intel used a class of artificial neural networks, the recurrent neural network or RNN that depends on previous calculations to work on current ones, to perform this artificial intelligence (AI) task.
This Neural Compute Stick is simply a case in point that Intel—a company which most people identify with central processing units (CPUs) inside personal computers (PCs), mobiles and servers—is widening its portfolio to stay in the AI race that has strong contenders including Nvidia, Microsoft, Google, Facebook, IBM, Amazon, Apple, Alibaba and Baidu.
At stake is an AI-focused hardware, software and services global market that is predicted to touch $58 billion market by 2021, according to International Data Corp. (IDC).
“Software is a critical component of our AI strategy,” insists Amir Khosrowshahi, vice-president and chief technology officer of AI Products Group (AIPG) at Intel. As part of its AI strategy, Intel offers its Xeon chip for data centres, Movidius for embedded vision and MobileEye for the automotive sector. It also has programmable chips called field programmable gate arrays, or FPGAs.
Microsoft’s hardware architecture called Project Brainwave, for instance, is deployed on Intel’s FPGA to help it make real-time AI calculations. Philips, on its part, uses Xeon Scalable processors to handle its AI workloads.
Intel also funds SETI@home, a programme that aims at detecting intelligent life outside Earth. “We are using AI techniques to augment the search,” says Khosrowshahi.
