Hardware for AI: TPUs, NPUs and Tensor Acceleration
💡 Quick Tip
Fact: An NPU in a smartphone is designed to consume 100 times less energy than a CPU when performing AI tasks.
The Need for Dedicated Silicon
Modern AI requires massive matrix multiplications and accumulations (MAC). While GPUs are versatile, the industry has created AI ASICs designed exclusively for tensors.
TPU (Tensor Processing Unit)
Developed by Google, TPUs use a Systolic Array architecture. Unlike a CPU where instructions access registers, data in a TPU flows through a mesh of calculation units continuously, performing hundreds of thousands of multiplications in a single cycle.
NPU (Neural Processing Unit)
NPUs are integrated AI accelerators in consumer chips (SoCs). They offload repetitive tasks like facial recognition or real-time translation from the CPU/GPU using a fraction of the battery.
Reduced Precision: FP16 and INT8
A key hardware technique is reduced precision. While scientific computing uses 64-bit, AI works well with 8-bit (INT8). This allows processing 4x more data in the same space.
📊 Practical Example
Real-World Scenario: Infrastructure Choice for an AI Startup
Step 1: Compatibility Analysis. Verify if code uses TensorFlow (optimized for TPUs) or PyTorch (where NVIDIA GPUs remain the most robust technical choice).
Step 2: Quantization Implementation. For deployment on client devices, convert the model from FP32 to INT8 (quantization).
Step 3: Edge Deployment. Thanks to quantization, the model now runs on a commercial tablet's integrated NPU, allowing fast diagnostics without sending private data to the cloud.
