Industrial IoT Spotlight · 2025-09-30 · 41 min
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 .
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