What Are AI Chips? Understanding NPU and TPU Technology and How They Power Modern Devices

🏷️Technology
⏱️18 min read
📅2025-01-01

What Are AI Chips? Understanding NPU and TPU Technology

Artificial intelligence has become the backbone of smartphones, computers, smart home devices, wearables, and even cloud services.
Features such as image enhancement, face recognition, voice assistants, real-time translation, and AI-powered photo/video editing rely on specialized AI processors.

The two most important types are:

  • NPU (Neural Processing Unit) – on-device AI accelerator
  • TPU (Tensor Processing Unit) – cloud AI accelerator for large models

This guide explains what they do, how they work, and why they are essential for the future of computing.

## Why Do We Need Dedicated AI Hardware?

General-purpose processors (CPU and GPU) are not optimized for heavy neural network workloads.
AI tasks require:

  • Massive parallel matrix multiplications
  • High memory bandwidth
  • Low-latency tensor operations

CPU can perform these tasks, but:

  • Far slower
  • Extremely inefficient
  • High battery consumption

This is why AI-accelerator chips exist.

✔ What Is an NPU?

An NPU is a hardware accelerator designed for local AI inference — meaning the AI model runs directly on the device.

Devices that use NPUs:

  • Smartphones
  • Tablets
  • Laptops
  • Wearables
  • Smart cameras
  • IoT systems

NPUs provide:

  • 10–20× faster AI compared to CPU
  • 5–10× better energy efficiency
  • Ability to run offline AI models

## What Tasks Does an NPU Handle?

✔ Computational photography

HDR fusion, night mode, portrait segmentation.

✔ Video enhancement

Stabilization, AI denoising, motion prediction.

✔ Facial recognition

Secure, local biometric processing.

✔ On-device LLMs

Running small to medium-sized language models locally.

✔ Voice recognition

Wake word detection, noise reduction, real-time transcription.

✔ Gaming optimizations

AI-based frame interpolation and scene enhancement.

## How Does an NPU Work?

NPUs are optimized for tensor operations:

  • Thousands of parallel arithmetic units
  • Local caches to store model weights
  • Very fast data paths for matrix multiplications
  • High throughput at low power

They are essentially the “mathematical engines” behind on-device AI.

✔ What Is a TPU?

A TPU is Google’s cloud-based AI chip designed specifically for training and inference of large-scale models.

Features:

  • Up to 100× faster than CPUs
  • Far more efficient than GPUs for matrix workloads
  • Supports massive models (100B+ parameters)
  • Used in Google's data centers for global AI services

## NPU vs TPU: Key Differences

| Feature | NPU | TPU | |---------|------|------| | Location | On device | Cloud/server | | Power usage | Very low (1–5W) | High (100–300W) | | Purpose | Inference | Training + inference | | Model size | Up to a few billion | 100B+ | | Latency | Instant | Depends on network |

Short version:

  • NPU = personal AI
  • TPU = global AI infrastructure

## Real-World Benefits of NPUs

✔ Faster user experience

Photos and videos process instantly.

✔ Longer battery life

AI offloaded from CPU/GPU.

✔ Offline privacy

Data does not leave the device.

✔ Real-time enhancements

AI video filters, translation, noise suppression.

✔ Personal LLMs

Small AI models running locally.

## Examples of NPU Hardware

🟦 Qualcomm Snapdragon

Snapdragon 8 Gen 3 → 45 TOPS NPU.

🟥 Apple Neural Engine

Integrated into A-series and M-series chips.

🟪 Intel AI Boost

Meteor Lake architecture with dedicated NPU tile.

🟧 Samsung Exynos

Advanced NPUs optimized for on-device AI.

🟩 MediaTek Dimensity

High-performance NPUs for camera, gaming, and voice AI.

## TPU Use Cases

  • Training Google’s Gemini / Bard models
  • YouTube automatic captioning
  • Google Photos object detection
  • Autonomous driving datasets
  • DeepMind reinforcement learning

TPUs are the backbone of cloud AI.

## The Future of NPUs

NPUs will soon enable:

✔ On-device LLMs

Private chat assistants without cloud dependency.

✔ Real-time video editing

AI stabilization, color grading, upscaling.

✔ AI-generated photos & videos

Created directly on the device.

✔ Better gaming experiences

AI frame generation similar to DLSS.

✔ Stronger data privacy

All processing stays local.

## Conclusion

NPU and TPU technologies are critical for the advancement of AI.
NPUs power personal, real-time AI experiences in everyday devices, while TPUs handle massive model training in the cloud.

Together, they form the foundation of the future AI ecosystem — faster, smarter, and more secure.