The development and upgrading of chips has always relied on the promotion of three aspects: process, architecture and application.
In terms of application, with the in-depth development and wide application of AI technology, different AI application scenarios are promoting the development of AI chips in the direction of specialization to meet the needs of performance, power consumption and cost in specific scenarios.
In terms of technology, with the development of mimicry neurons, quantum and other technologies,AI chips are constantly breaking through the constraints of traditional architectures and processes on performance, exploring and innovating in different technological paths, and showing diversified development directions.
AI scenarios and algorithms are driving AI chips toward specialization
Driven by AI algorithms and application scenarios, features, FPgas and ASics are showing a development direction characterized by meeting the needs of specialization.
(1) It performs well in handling a large number of parallel computing tasks, and can better play the potential of AI through accelerated design, but it also has the disadvantages of high power consumption and high cost. At present, technology is still the main hardware choice for the computing power required for AI training.
(2) FPGA has strong computing power, low trial and error cost and enough flexibility, but its disadvantages are high price and complex programming, so it has advantages in semi-customized AI scenarios.
(3) ASics have higher processing speed and lower energy consumption, and can be optimized for specific AI tasks, so that they have better comprehensive qualities in terms of performance and energy consumption, which makes them excellent in fully customized AI scenarios.
Brain-like and quantum technologies promote the diversification of AI chips
With the development of cutting-edge technologies such as mimicrous neurons and quantum,AI chips have gradually developed new chips with diversified technology paths such as brain and quantum, and brain-like chips have begun to be commercialized.
(1) Brain-like chips have the technical potential of massively parallel computing, ultra-low power consumption and ultra-low latency, which make them play an important role in future AI application scenarios. In the future, an important development direction of brain-like chips is to build a more efficient storage and computing integrated computing system around the daily algorithm, such as the development of more efficient chip architecture, chips with more neurons, etc., in order to continuously upgrade the comprehensive performance of AI chips.
(2) Quantum chips are chips built on the principles of quantum mechanics, which can promote the exponential growth of human computing power and form "quantum superiority". Some experts believe that quantum chips are expected to completely solve the problem of AI computing power bottleneck.
In the future, with the wide application of AI, the demand and power consumption of the entire society for AI computing power will increase significantly, and quantum chips are potential solutions to the above series of problems. However, the current development of quantum computers still faces problems such as decoherence, resulting in the current quantum chips still mainly exist in the laboratory stage, far from commercialization.
In general, brain-like chips and quantum chips, as new chip technologies, have great potential and will play an important role in the field of AI and computing in the future, bringing us more efficient and powerful computing power.