In this computing paradigm, the computing unit can only process one task at a certain interval and wait for memory to update its results, because both data and instructions are stored in the same memory space, which greatly limits the throughput and causes idle power consumption. Although mechanisms like cache and branch prediction can partially eliminate the issues, the "memory wall" still poises a grand challenge for the massive data interchanging in modern processor technology. To break "memory wall," in-memory processing has been studied since 2000s and regarded as a promising way to reduce redundant data movement between mem-ory and processing unit and decrease power consumption. The concept has been implemented with different hardware tools, e.g., 3D-stack dynamic random access memory (DRAM) [69] and embedded FLASH [70]. Software solutions like pruning, quantization and mixed precision topologies are implemented to reduce the intensity of signal interchanging.
The need of in-memory computing, Page 1 of 2
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