Application of computing in memory chip in motion recognition

  Computing in memory refers to the fusion of storage and computing functions into a single chip, enabling storage and computation to be performed on the same chip. In motion recognition, storage-compute integration can provide the following benefits:

· Faster algorithm execution: Computing in memory allows algorithms to be executed internally within the chip, avoiding the time and energy consumption associated with transferring data from external devices to the processor, thereby speeding up algorithm execution.

· Lower power consumption: In computing in memory systems, data processing can be completed on the same chip, reducing energy consumption associated with data transfer and processing.

· Better privacy protection: Computing in memory architecture allows for data to be processed locally, avoiding the need to transmit data to the cloud for computation, thus protecting user privacy.

· Higher reliability: In computing in memory systems, algorithms can be tightly integrated with the chip, thereby improving system reliability and stability.

· Greater applicability: Computing in memory can be applied to various algorithms and models, thereby enhancing system applicability and scalability.

· Smaller device size:  Computing in memory can integrate storage and computing functions into the same chip, reducing device size and weight, and enhancing device portability.

In summary, the combination of  Computing in memory and algorithms can improve algorithm execution speed, energy efficiency, privacy protection, reliability, applicability, and device portability.

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