Specialized AI Hardware: TPUs, NPUs, and Neuromorphic Chips

Beyond GPUs, dedicated hardware accelerates AI and reduces power. This sub-article expands Section 1.3 of Hardware & Software.

TPUs (Tensor Processing Units)

• Google’s custom ASICs for tensor operations.
• Optimized for large Transformer training and inference.
• Co-designed with TensorFlow; deployed in Google Cloud clusters.

NPUs (Neural Processing Units)

• Consumer chips (Apple Neural Engine, Huawei NPU, Qualcomm AI) accelerate on-device inference.
• Trade flexibility for efficiency; enable edge AI and privacy-preserving processing.

FPGAs and ASICs

• FPGAs are reprogrammable, useful for specialized low-latency workloads.
• ASICs are fixed-function and maximally efficient for one workload.

Neuromorphic Computing

• IBM TrueNorth and Intel Loihi emulate spiking neural networks.
• Event-driven, ultra-low-power sensing and control.
• Still niche; programming and accuracy remain challenges.

Why It Matters

Specialization cuts cost and energy for both training and inference, enabling scale that general-purpose hardware cannot sustain.

Sources

• Google. Cloud TPU documentation.
• Intel. Loihi documentation.
• IBM. TrueNorth overview.

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