Artificial intelligence (AI) refers to the technology and application fields in which machines learn, reason, judge and make decisions by simulating the ability of human intelligence. along with TLV70450DBVR With the continuous development and application of artificial intelligence, the requirements for chip architecture are getting higher and higher. Next, we will discuss why AI needs a new chip architecture from the following aspects.
1. High performance computing requirements: AI tasks usually require a large number of computing resources, such as neural network training and reasoning processes in deep learning. Traditional general-purpose processors (such as CPUs) have low efficiency in processing these computationally intensive tasks, so new chip architectures are needed to provide higher computing performance. The new generation of AI chips, such as GPU and special AI chips (such as Google's TPU, NVIDIA's Tensor Core, etc.), have more parallel computing units and special hardware accelerators, which can provide higher computing performance.
2. Low power consumption and high energy efficiency requirements: AI applications usually need to run for a long time, and have high requirements for power consumption and energy efficiency. Traditional general-purpose processors generate a lot of heat under high load, resulting in high power consumption. The new AI chip can achieve better power consumption and energy efficiency performance under the same computing task by optimizing the architecture and adopting energy-saving technology.
3. Massive parallel computing requirements: Massive parallel computing in AI tasks is an important means to improve computing efficiency. Traditional general-purpose processors are limited by architecture, memory bandwidth and other factors when dealing with parallel computing, and it is difficult to give full play to performance. The new AI chip can better support large-scale parallel computing and improve computing efficiency by adding parallel computing units and optimizing memory access.
4. Specific hardware requirements: AI tasks often require specific types of hardware support, such as floating point computing, matrix multiplication, vector operations, etc. Traditional general-purpose processors are not optimized for these specific requirements, so they cannot provide efficient computing performance. The new AI chip can better meet the specific hardware requirements of AI tasks by adding special hardware accelerators and optimizing instruction sets.
5. Real time computing requirements: some tasks in AI applications require real-time computing capabilities, such as perception and decision-making in automatic driving. Traditional general-purpose processors have great limitations in real-time computing, and can not meet the real-time requirements. The new AI chip can provide better real-time computing capability and meet the needs of real-time applications by optimizing the architecture and adding special hardware.
To sum up, AI needs a new chip architecture mainly to provide higher computing performance, lower power consumption and high energy efficiency, better parallel computing support, meet specific hardware requirements, and provide real-time computing capabilities. The new AI chips can better meet the needs of AI applications and promote the development and application of AI technology by means of optimizing the architecture, increasing hardware accelerators and special hardware support.
