Neural Processing Unit (NPU)

The Neural Processing Unit (NPU) is a dedicated hardware component within the Main Processing Unit that efficiently executes neural network computations, enabling faster AI inference and training tasks compared to general-purpose CPUs or GPUs. It is used in computer, electronic, and optical product manufacturing as a component-level part. The NPU employs parallel processing architectures optimized for matrix and vector operations common in neural networks, using specialized circuits to perform tasks like convolution, pooling, and activation functions with high energy efficiency. Typical parameters include computing power of 8–32 TOPS, power consumption of 2–15 W, operating temperature of -40–85 °C (per IEC 60068-2-14), supply voltage of 0.8–1.2 V, process node of 7–16 nm, memory bandwidth of 25.6–102.4 GB/s, precision support of INT8/FP16, package dimensions of 15×15–45×45 mm (per JEDEC MS-028), weight of 5–50 g, operating humidity of 10–90 %RH (per IEC 60068-2-78), ESD tolerance of ±2000 V (per IEC 61000-4-2), and MTBF of 100000–500000 h (per Telcordia SR-332). These values are directory reference ranges and must be confirmed for the actual model and application. The NPU is typically fabricated on silicon. Selection inputs include required TOPS for target models, thermal design power, operating environment, and interface compatibility. Verification questions should address model-specific performance, thermal management, and compliance with listed standards. Maintenance signals include thermal throttling, performance degradation, or failure to meet inference latency. Failure boundaries include exceeding operating temperature or humidity limits, which may cause damage or reduced reliability. Always verify model-specific values and standards with the legal manufacturer or supplier.

The NPU uses parallel processing architectures optimized for matrix and vector operations common in neural networks. It employs specialized circuits to perform convolution, pooling, and activation functions with high energy efficiency. By executing these operations in parallel, the NPU accelerates AI inference and training tasks compared to general-purpose CPUs or GPUs.