EP 226 - Neuromorphic for LLMs on the Edge

In this episode, we spoke with Sean Hehir , CEO, and Jonathan Tapson , Chief Development Officer, of BrainChip about neuromorphic computing for edge AI. We covered why event-based processing and sparsity let devices skip 99% of useless sensor data, why joules per inference is a more honest metric than TOPS, how PPA (power, performance, area) guides on-device design, and what it will take to run a compact billion-parameter LLM entirely on device. We also discussed practical use cases like seizure-prediction eyewear, drones for beach safety, and efficiency upgrades in vehicles, plus BrainChip's adoption path via MetaTF and its IP-licensing business model. Key insights : • Neuromorphic efficiency . Event-based compute minimizes data transfer and optimizes for joules per inference, enabling low-power, real-time applications in medical, defense, industrial IoT, and automotive. • LLMs at the edge . Compact silicon and state-based designs are pushing billion-parameter models onto devices, achieving useful performance at much lower power. • Adoption is designed to be straightforward . Models built in standard frameworks can be mapped to BrainChip's Akida platform using MetaTF, with PPA guiding silicon optimization and early evaluation possible through simulation and dev kits. • Compelling use cases . Examples include seizure-prediction smart glasses aiming for all-day battery life in a tiny form factor and drones scanning beaches for distressed swimmers. Most current engagements are pure on-edge, with hybrid edge-plus-cloud possible when appropriate. IoT ONE database : The Industrial IoT Spotlight podcast is produced by Asia Growth Partners (AGP) :

You can listen to EP 226 - Neuromorphic for LLMs on the Edge online on Radio and Podcast. Open the player on this page to stream the available audio.

EP 226 - Neuromorphic for LLMs on the Edge is an episode from Industrial IoT Spotlight by IoT ONE.

This episode is 40:56 long.

This episode was published on Sep 30, 2025.

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