5 Cool Chip Solutions For Edge AI

As the hype around generative AI has created new demand for powerful chips in data centers, the need for smaller and more efficient chips remains strong as organizations plan to spend larger sums of money on IoT deployments that will require edge AI processing capabilities.

Last month, research firm Gartner said increased use of AI-based applications in edge infrastructure and endpoint devices is a significant driver in its estimate that AI chip revenue will grow 20.9 percent this year to $53.4 billion. Sales are expected to expand further, by 25.6 percent, to $67.1 billion in 2024.

[Related: AMD Acquires Mipsology To Ramp Up AI Inference Rivalry With Nvidia]

For the massive number of IoT devices coming online, processing AI workloads at the edge is better than the cloud for providing near-real-time inference and response times in addition to slashing data transport and processing costs and ensuring data privacy and security, senior staff at global consulting firm Deloitte wrote in a recent article for The Wall Street Journal.

“By distributing the cloud’s scalable and elastic computing capabilities closer to where devices—and users—generate and consume data in the physical world, leading-edge technologies such as 5G, edge computing, and computer vision can enable enterprises to modernize applications across operational sites and help to enhance customer experience, operational efficiency, and productivity,” they said.

As part of CRN’s IoT Week 2023, here are five cool chip solutions for edge AI applications from leading and emerging vendors in the semiconductor space: AMD, Axelera AI, Intel, Nvidia and SiMa.ai.