Hardware Podcasts

437: Chirping With the Experts

Embedded.fm · 8 Dec 2022 · 1h 05m · Episode 437

What this episode covers

Daniel Situnayake, Head of Machine Learning at Edge Impulse and co-author of the O'Reilly book 'AI at the Edge', discusses the practical application of embedded machine learning. He explains the BLERP framework (Bandwidth, Latency, Economics, Reliability, Privacy) for deciding when to deploy AI on edge devices and emphasizes the importance of responsible AI, including hiring ethicists and using diverse datasets. The conversation also covers the current state of large language models like ChatGPT, noting their utility as creative tools with human-in-the-loop oversight rather than autonomous agents.

Most relevant to

companies scaling up

Topics

Mentioned

Companies
Edge Impulse, O'Reilly Media, Google, ARM
Products
AI at the Edge, TinyML, ChatGPT, AI Dungeon
Tools
TensorFlow Lite

Related episodes from other shows

Other shows on the same ground, closest first.

  • Coredump Sessions · 57m

    #013: Prepping Your AI Model for the Wild: Building Edge AI Models That Work in the Real World

    David Tischler and Alessandro Grande from Edge Impulse discuss the practical realities of deploying Edge AI models on constrained devices, emphasizing that customization and fine-tuning are the primary value propositions over raw model complexity. They explain how effective Edge AI requires a combination of traditional DSP pre-processing and ML models, and why hardware selection must balance compute power with software ecosystem usability. The conversation highlights the critical need for observability and OTA updates to monitor model drift and push fixes in the field, as models rarely work perfectly out of the box.

    AI & machine learningMicrocontrollers & dev platforms
    7 chapters

  • Coredump Sessions · 55m

    #007: AI, Open Source, and the Future of Embedded Development: How Much Code Will We Actually Write?

    François Baldassari, CEO of Memfault, and Thomas Sarlandie, Field CTO of Memfault, host a panel with Jonathan Beri, CEO of Golioth, and Dan Mangum, CTO of Golioth, to discuss the intersection of AI, open source, and embedded development. The group analyzes the maturity of edge AI for audio and video processing, the challenges of integrating LLMs into hardware due to rapid model iteration, and the current state of using AI for firmware development. They conclude that while AI is transforming code generation and review, open source remains critical for training models and providing trusted, complex infrastructure like Zephyr and FreeRTOS.

    Embedded firmwareAI & machine learning
    9 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

  • 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

  • OnTrack · 41m

    Edge Devices and LLMs: What's Ahead for AI

    Jan Jongboom, co-founder of Edge Impulse, discusses the feasibility of running Large Language Models (LLMs) on edge devices by reducing their size through knowledge distillation and synthetic data generation. He explains that while general-purpose MCUs can handle small, specialized models, RAM capacity is often the primary constraint rather than compute power. Jongboom highlights techniques like using GPT-4 to label data for training smaller, hyper-specific models that fit on microcontrollers like the Cortex-M4.

    AI & machine learningMicrocontrollers & dev platforms
    5 chapters

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