An in-memory computing system supporting general computing based on magnetic random access memory

Abstract

The invention belongs to the field of general-purpose in-memory computing, and in particular relates to an in-memory computing system based on magnetic random access memory and supporting general-purpose computing. decoder, row decoder, column decoder, bit line driver, calculation word line digital-to-analog converter, precharge sense amplifier, fifth generation RISC processor, instruction parser and registers. The present invention can not only implement storage in the memory, but also perform calculation operations in the memory. In addition, the present invention can effectively support general-purpose calculations (including logic calculations, fixed-point calculations, floating-point calculations, etc.), make full use of multiple sub-array structures and improved shifters and connectors, and improve the reconfigurability of the architecture and the parallelism of calculations , to improve computational efficiency.

Description

technical field [0001] The invention belongs to the field of general in-memory computing, in particular to an in-memory computing system and a computing method for general computing based on spin transfer torque magnetic random access memory (STT-MRAM). Background technique [0002] Over the past few decades, the size of datasets has grown exponentially over time, placing ever greater computational demands on data analysis applications. However, for traditional von Neumann architectures, the overhead of data communication between processors and memory units leads to huge performance degradation and energy consumption, known as the von Neumann bottleneck. [0003] In order to overcome the data communication bottleneck, a widely recognized method is to embed the processor in the memory, that is, in-memory computing. Some studies have proposed in-memory computing solutions based on SRAM and DRAM. Although the data communication overhead has been greatly reduced, as volatile me...

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Problems solved by technology

Some studies have proposed in-memory computing solutions based on SRAM and DRAM. Although the data communication overhead has been greatly reduced, as volatile memories, the static power consumption of SRAM and DRAM has become an important factor affecting their performance, making it difficult to meet future requirements. Ultra-low power consumption requirements based on big data application scenarios

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