As end-to-end (E2E) autonomous driving architectures gradually become mainstream, the inference demands of physical AI are driving radical transformations in automotive system-on-chip (SoC) design. DIGITIMES Intelligence analyst Jasper Jiang notes that neural processing units (NPUs), or dedicated AI accelerators, are becoming the core processing engines of next-generation automotive SoCs to satisfy three stringent demands of AI inference in autonomous driving: ultra-low latency, reduced system p
As end-to-end (E2E) autonomous driving architectures gradually become mainstream, the inference demands of physical AI are driving radical transformations in automotive system-on-chip (SoC) design. DIGITIMES Intelligence analyst Jasper Jiang notes that neural processing units (NPUs), or dedicated AI accelerators, are becoming the core processing engines of next-generation automotive SoCs to satisfy three stringent demands of AI inference in autonomous driving: ultra-low latency, reduced system pThis article was originally published by Digitimes.
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