Hardware Podcasts

Chips at the Edge: Innovation for AI at Edge and Automotive

EE Times Current · 14 Jan 2026 · 25m

What this episode covers

Hezzy Tzar, Executive Director at Synopsys, discusses the shift of AI workloads from the cloud to the edge, highlighting the critical balance of power, performance, and cost in devices like smartphones and automotive systems. He explains that memory bandwidth, specifically involving LPDDR6 and UFS, is the primary bottleneck for on-device AI, necessitating flexible multi-die architectures and standards like UCIe to manage scalability and reliability in automotive applications. Tzar emphasizes the importance of 'shift left' methodologies, using emulation and FPGA prototyping to handle rapid model changes, and predicts a future where hardware must support continuous software upgrades and subscription-based models for physical AI devices like robotics.

Most relevant to

large enterprises

Topics

Mentioned

Companies
Synopsys, Astra
Also
UCIe, LPDDR6, UFS

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