It is just at the beginning of the explosion of demand for neural networks and machine learning processing. Traditional CPUs/GPUs can do similar tasks, but NPU specifically optimized for neural networks can perform much better than CPUs/GPUs. Gradually, similar neural network tasks will be done by dedicated NPU units.
NPU
NPU (neural processing unit) is a specialized processor for network application packets, using a "data-driven parallel computing" architecture, especially good at processing massive multimedia data such as video and images.
NPU is also a kind of integrated circuit, but different from the single function of special-purpose integrated circuit (ASIC), network processing is more complex, more flexible. Generally, we can use software or hardware in accordance with the characteristics of network computing special programming to achieve the special purpose of the network.
The highlight of the NPU is the ability to run multiple parallel threads -- The NPU is taken to another level with some special hardware-level optimizations, such as providing some easily accessible caching systems for some really different processing cores. These high-capacity cores are simpler than typical "regular" processors because they don't need to perform multiple types of tasks. This set of "optimizations" makes NPUs more efficient, which is why so much R&D is being put into ASICs.
One of the advantages of NPUs is that they spend most of their time focusing on low-precision algorithms, new data flow architectures, or in-memory computing capabilities. Unlike GPUs, they are more concerned with throughput than latency.
