Paul Schell

Paul Schell

Industry Analyst

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Topics Covered

RISC-V for Edge AI Applications

Paul Schell In The News

CNN (2024-05-28)
Samsung’s “circle to search” feature, which allows users to quickly search for information on a device’s screen with a finger gesture, has received a lot of attention and is featured in marketing campaigns. Multimodal features – which refers to an AI system that can interpret and generate different types of data, such as text and images at the same time — like analysis of video footage and in-call spam detection could also form part of the tools, according to Paul Schell, industry analyst at tech intelligence firm ABI Research. “Something similar would likely be included in an Apple offering, given its relative simplicity and appeal that goes beyond simple image search,” Schell said. “But verbal interactions with a bot like Siri will be much more natural and fluent, and its capabilities will go far beyond the previous, narrow domains, like news and weather updates.”
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Tech News World (2024-03-19)
“Apple appears to be behind its competitors in addressing generative AI, and this is partly because the pace of innovation has been so high that the timings of its yearly developer conference in summer and product release in autumn have created a mismatch at the current pace in the AI race,” said Paul Schell, an industry analyst with global technology intelligence firm ABI Research.
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Information Week (2024-03-12)
“The novel thing about this innovation cycle is on-device, generative AI,” says Paul Schell, industry analyst with ABI Research. Though AI has been on-device for some time, he says, its earlier iterations dealt with simpler, lighter workloads for such tasks as image enhancement or gaming applications. AI chipsets could make AI more attractive to industries that have been reluctant to adopt the technology, according to Reese Hayden, senior analyst with ABI Research.
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Wire19 (2024-02-22)
“Cloud deployment will act as a bottleneck for generative AI to scale due to concerns about data privacy, latency, and networking costs. Solving these challenges requires moving AI inferencing closer to the end-user – this is where on-device AI has a clear value proposition as it eliminates these risks and can more effectively scale productivity-enhancing AI applications,” says Paul Schell, Industry Analyst at ABI Research. “What’s new is the generative AI workloads running on heterogenous chipsets, which distribute workloads at the hardware level between CPU, GPU, and NPU. Qualcomm, MediaTek, and Google were the first movers in this space, as all three are producing chipsets running LLMs on-device. Intel and AMD lead in the PC space.”
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