Let you know in detail what computing power is ？

Fancy Wang 1905 2022

Artificial intelligence chips refer to computing chips that can accelerate various artificial intelligence algorithms. The needs of deep neural networks for computing chips are mainly reflected in two aspects:

• communication of massive data between chips and storage, including cache and on-chip storage, data interaction between computing units and storage;
• Convolution, residual, and fully connected computing types require a lot of computing, which can not only improve the computing speed, but also reduce power consumption. In the model training process, apart from using CPU or GPU for processing, many companies have developed and used field programmable gate arrays (FPGA) and application-specific integrated circuits (ASIC) for different scenarios and algorithms. In the terminal inference process, ASIC is mainly used. .

CPU is not suitable for deep learning training scenarios. Early deep learning scenarios were built on CPUs. However, since the CPU itself is a general-purpose calculator, its main advantages focus on management, scheduling and coordination capabilities, and the number of computing units available for floating-point calculations is too small to meet the needs of deep learning, especially the large number of floating-point operations in the training process, and Data communication between CPU threads requires access to global memory, and parallel computing efficiency is too low.

GPU has become the first choice for deep learning training scenarios due to its performance advantages. The key performance of GPU is its powerful parallel computing capability.

The main reasons why it is suitable for deep learning computing are as follows:

• the high-bandwidth shared cache effectively improves the efficiency of mass data communication, and the GPU can realize the communication between threads by accessing the shared memory. Data communication; using multiple computing cores to improve parallel computing capabilities. The GPU has a large number of computing cores, and the application throughput is 100 times that of the CPU.

FPGA can be reconfigurable and customizable in terms of speeding up deep learning operations.

Its main advantages are:

• powerful computing power, it can generate special circuits by editing and recombining circuits for a certain operation, thereby greatly compressing the computing cycle;
• Low power consumption, FPGA power consumption ratio is 3 times that of GPU;
• high flexibility, FPGA can easily implement the underlying hardware control operation technology, leaving more space for algorithm function realization and algorithm optimization.

The main disadvantage of FPGA:

FPGA applications often need to support a large data throughput, which requires high memory, bandwidth and I/O interconnection bandwidth

ASIC is a highly customized special-purpose computing chip, which is higher than FPGA in performance.

We are a 100G switch with Nos, 100G module/network card factory in Shenzhen, China. We can provide you with one-stop service on products, transportation, customs clearance, and tariffs.