Ambiphilic cross-coupling with aryl-bismuth reagents

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4. Microscopy & Examination

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Gregory Benton, New York University,这一点在zoom中也有详细论述

TPUs, developed by Google, go further by specializing in tensor operations with systolic array architectures, delivering higher efficiency for both training and inference in structured AI workloads. NPUs push optimization toward the edge, enabling low-power, real-time inference on devices like smartphones and IoT systems by trading off raw power for energy efficiency and latency. At the far end, LPUs, introduced by Groq, represent extreme specialization—designed purely for ultra-fast, deterministic AI inference with on-chip memory and compiler-controlled execution.

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