The Future of Edge Computing and AI | Axelera AI's Fabrizio Del Maffeo | Internet of Things Podcast

IoT For All Podcast · 11 Feb 2025 · 17m · Episode 370

What this episode covers

Fabrizio Del Maffeo, co-founder and CEO of Axelera AI, explains the shift from cloud-based AI to edge AI, emphasizing benefits like reduced latency, real-time decision-making, and enhanced privacy. He details how Axelera AI’s hardware accelerates computer vision and generative AI models on edge devices, addressing challenges in model compression and power efficiency. The conversation covers retrofitting existing infrastructure with AI, the complementary role of cloud computing, and industry trends observed at Super Computing 2024, including the rise of inference-focused hardware alternatives to Nvidia.

Other shows on the same ground, closest first.

  • EE Times Current · 45m

    From Data Centers to Devices – The Ongoing [R]Evolution of AI Computing

    Matt Gutierrez, Senior Director of Marketing at Synopsys, discusses the migration of AI computing from centralized data centers to the network edge. He explains that this shift is driven by the need for low latency, data privacy, and power efficiency, enabled by smaller neural networks and specialized hardware like NPUs and TPUs. Gutierrez details how Synopsys supports this through its ARC processor IP portfolio and EDA tools, helping engineers manage the complexity of heterogeneous processing platforms for edge devices.

  • Over the Air · 24m

    AI at the Edge: Justin Schneck on the Future of Embedded Devices

    Justin Schneck, co-founder and CPO of Peridio, discusses the evolution of AI from cloud to edge, introducing Peridio's upcoming 'Avocado' OS designed to simplify embedded product development. He and host Ryan Prosser of Very explore the spectrum from cloud AI to embedded AI, noting how specialized silicon and miniaturized models are enabling practical applications like Garmin's radar-enabled bike lights. Schneck argues that while cloud AI handles massive compute, the real value lies in robust, self-initializing embedded systems that reduce hardware complexity for developers.

  • The Circuit · 34m

    Episode 19: The On-Device AI Debate

    Ben Bajarin and Jay Goldberg discuss the economic and technical drivers shifting AI workloads from the cloud to on-device processing, highlighting companies like Qualcomm, Apple, and NVIDIA. They analyze the trade-offs between cloud-based model size and accuracy versus edge-based privacy and cost, noting that models like Llama and Stable Diffusion are being optimized for devices like the iPhone and Raspberry Pi. The hosts argue that while cloud AI offers superior capability, the high cost of inference and privacy concerns are pushing developers to adopt hybrid approaches and optimize silicon for power efficiency rather than raw throughput.

  • The Robot Report Podcast · 1h 16m

    Edge Computing and AI: A Conversation with Ben Wolff

    Ben Wolff, CEO of Palladyne AI (formerly Sarcos), discusses the company's pivot from hardware to a software-only model, focusing on Palladyne IQ for industrial robots and Palladyne Pilot for autonomous drone swarming. He explains the strategic advantages of edge computing for latency and security, the challenges of a 12-18 month sales cycle, and the company's commitment to building hardware-agnostic AI solutions that are indispensable to customers.

  • Coredump Sessions · 58m

    #004: The Future of Edge AI and What it Means for Device Makers

    Alex Samuelson, CTO and co-founder of Imagimob, joins Memfault founders Francois Baldassari and Chris Coleman to discuss the practical implementation of Edge AI on embedded devices. They highlight how hardware accelerators like the Cortex-M55 and U55 have enabled GPU-like performance on microcontrollers, allowing for real-time inference in applications such as welding quality monitoring and fall detection. The conversation emphasizes that data collection, labeling, and memory constraints are the primary bottlenecks, requiring device makers to build data acquisition firmware early in the development process. Finally, they discuss the shift toward 'AI as a Service' business models, where hardware companies generate recurring revenue by updating models over the device's lifecycle.

The summary, chapters and topics are generated from the episode’s audio by this directory, not written by IoT For All Podcast. The episode itself belongs to its publisher.