Hardware Podcasts

Ep. 11 | Real-Time Edge Processing: Get to Market Faster with Future-Proofed Designs

Moore's Lobby · 29 Oct 2020 · 49m · Episode 11

What this episode covers

Richard Barry, founder of FreeRTOS and senior principal engineer at Amazon Web Services, discusses the shift toward real-time edge processing in IoT devices. He explains how microcontrollers are handling more complex workloads, including machine learning and connectivity, which creates significant knowledge gaps for engineers specializing in single domains like motor control. Barry details AWS's fleet provisioning strategies, such as Just-in-Time Registration and Fleet Provisioning by Claim, to secure devices at scale without pre-registration. He emphasizes future-proofing designs through Over-the-Air (OTA) updates and using pre-integrated, open-source libraries like FreeRTOS to manage undifferentiating functionality.

Most relevant to

large enterprises

Topics

Mentioned

Companies
Amazon Web Services, IAR
Products
FreeRTOS, AWS IoT, GreenGrass
Tools
PKCS 11, TLS, MQTT

Related episodes from other shows

Other shows on the same ground, closest first.

  • Coredump Sessions · 55m

    #019: END-OF-YEAR CELEBRATION: Moments You Loved, Stories You Missed, and 2026 Predictions You Need

    François Baldassari and Chris Coleman, co-founders of Memfault, reflect on the 20th episode of Coredump Sessions, highlighting lessons from guests like Nick Sinas of Oval, Dan Mangum of Golioth, and Chad from Gabb. They discuss the critical importance of Over-the-Air (OTA) updates, the rise of AI-assisted firmware development using tools like Claude and Cursor, and the evolving security landscape for devices from Pebble to modern smart rings. The hosts predict that 2026 will see OTA become a regulatory requirement and AI offload processing to the edge, while emphasizing the need for engineers to adapt to higher-level abstractions.

    Connected products & IoTAI & machine learning
    11 chapters

  • EE Times Current · 38m

    Taming the Wild Edge

    Chris Catterton, head of solution engineering at Micro AI (formerly OneTech), explains how their Adam ML technology enables lightweight AI training and inference on resource-constrained microcontrollers at the extreme edge of IoT. He details how this approach solves high data transmission costs by processing data locally and only sending insights to the cloud, using examples from oil rigs and manufacturing lines. Catterton also describes how the same behavioral profiling technology provides endpoint security by detecting anomalies in CPU and memory usage to prevent ransomware attacks and other threats.

    Embedded firmwareSecurity & privacy
    6 chapters

  • 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.

    AI & machine learningMicrocontrollers & dev platforms
    8 chapters

  • EE Times Current · 29m

    AI and Connectivity at the Edge and Endpoint

    Kaushal Vora and Mo Dogar from Renesas discuss the convergence of AI and IoT (AIoT) at the edge, distinguishing between resource-intensive generative AI and predictive AI suited for constrained endpoint devices. They highlight Renesas' acquisition of Reality AI and its AutoML toolchain, which allows embedded engineers to optimize machine learning models for microcontrollers without adding hardware sensors, as demonstrated in washer and battery management use cases. The guests explain how decentralizing intelligence reduces latency, power consumption, and security risks by performing inference locally on devices like the RH850 microcontroller.

    Embedded firmwareMicrocontrollers & dev platforms
    8 chapters

  • IoT For All Podcast · 31m

    IoT Device Security throughout a Solution's Lifecycle | Twilio’s Evan Cummack

    Evan Cummack, General Manager of IoT & Wireless at Twilio, explains how the company applied its developer-first API model to cellular IoT through products like Programmable Wireless, Super SIM, and the acquisition of Electric Imp. He discusses the challenge of 'serviceification' in IoT, citing examples like Pulsa sensors and San Sonio, and introduces Microvisor as a solution to the difficulty of managing device security and OTA updates. Cummack argues that hardware innovation is slowed by the steep climb from general-purpose to single-purpose computing, urging developers to use managed platforms for non-differentiating tasks like certificate management.

    Security & privacyConnected products & IoT
    7 chapters

The summary, chapters and topics are generated from the episode’s audio by this directory, not written by Moore's Lobby. The episode itself belongs to its publisher.