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Artificial intelligence (AI) models like ChatGPT run on algorithms and have great appetites for data, which they process through machine learning, but what about the limits of their data-processing abilities? Researchers led by Professor Sun Zhong from Peking University's School of Integrated Circuits and Institute for Artificial Intelligence set out to solve the von Neumann bottleneck that limits data-processing.

In their paper published in the journal Device on September 12, 2024, the team developed the dual-IMC (in-memory computing) scheme, which not only accelerates the machine learning process, but also improves the energy efficiency of traditional data operations.

When curating algorithms, software engineers and computer scientists rely on data operations known as matrix-vector multiplication (MVM), which supports neural networks. A neural network is a computing architecture often found in AI models that mimics the function and structure of a human brain.

As the scale of datasets grows rapidly, computing performance is often limited by data movement and speed mismatch between processing and transferring data. This is known as the von Neumann bottleneck. The conventional solution is a single in-memory computing (single-IMC) scheme, in which neural network weights are stored in the memory chip while input (such as images) is provided externally.

However, the caveat to the single-IMC is the switch between on-chip and off-chip data transportation, as well as the use of digital-to-analog converters (DACs), which cause a large circuit footprint and high power consumption.

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