This article systematically breaks down four key processing units shaping today’s computing landscape: CPUs, GPUs, NPUs, and TPUs.

CPUs excel at complex sequential tasks due to few powerful cores with layered cache.

GPUs leverage thousands of simpler cores for massive parallelism, ideal for AI training via optimized throughput.

NPUs specialize in edge AI inference with brain-inspired architectures, trading generality for energy-efficient neural computations on mobile.

TPUs utilize systolic arrays to accelerate large-scale AI workloads in the cloud but remain tied closely to TensorFlow ecosystems.

I appreciate the nuanced emphasis on architectural trade-offs and evolving integration trends. For those deploying heterogeneous systems, what’s your experience with managing software complexity across diverse processing units?

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