Computing in Memory: The Future of AI Deep Applications (Internet Services - Other Internet Services)
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Computing in Memory: The Future of AI Deep Applications | |
Artificial intelligence (AI) has made significant progress over the past few years, and it owes much of its success to the use of deep learning algorithms. However, these algorithms require vast amounts of data to be processed, which becomes an issue when the data sets exceed the capacity of the central processing units (CPUs) and graphics processing units (GPUs). This is where computing in memory (CiM) comes in – a technology that provides a possible solution to this problem. CiM is a processing technique where computation is done in the memory itself, rather than moving data between the memory and the central processing unit. This eliminates the need for the traditional memory hierarchy and increases the processing speed while reducing power consumption. CiM is also referred to as in-memory computation or processing in memory (PIM). CiM is considered the future of AI deep applications because it allows for faster data processing and analysis. This technology can significantly speed up the process of training AI models, as it reduces the latency involved in moving data between memory and the processing unit. It is particularly useful when dealing with large-scale data sets, such as those used in speech recognition, image processing, and natural language processing. Advancements in CiM technology have been made possible by the development of advanced memory technologies such as the dynamic random-access memory (DRAM) and the phase-change memory (PCM), both of which can support CiM operations. DRAM is widely used in computer systems and is a type of volatile memory that stores data temporarily. PCM, on the other hand, is a type of non-volatile memory that can store data permanently and can be used as a replacement for traditional hard drives. CiM technology has already been used in some AI applications. An example is the development of neuromorphic chips, which are designed to emulate the human brain's neural circuits. These chips utilize in-memory computing, which allows for faster and more efficient processing of the vast amounts of data involved in machine learning. Another example of CiM technology is a system called CogniMem, which uses in-memory computing to enable real-time image recognition with high accuracy at low power consumption. This system has been used in smart cameras, self-driving cars, and other AI applications. In conclusion, CiM technology has the potential to revolutionize AI deep applications by providing faster and more efficient data processing and analysis. With the constant development of advanced memory technologies, we can expect to see further advancements in CiM technology and its integration into various AI applications. As such, it is an exciting time for AI research and development, and CiM is one of the technologies that is leading the way forward. computing in memory (8).jpg | |
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2024-04-29 (0.398 sec)