Using In-Memory for intelligent Computing tech to Improve Deep Learning models in GCC

To speed up fundamental computing activities like matrix-vector multiplication, in-memory computing (IMC) executes numerical calculations utilizing physical processes like charge collecting and current summation. IMCs have a great lot of promise for memory-intensive processing applications like deep learning and machine learning, but their design and implementation are quite difficult because of nonidealities in circuits and devices. An overview of current options and research prospects for deep learning accelerators employing novel memory technologies that might be used via IMC is provided in this article. The prerequisites include device technology, computer primitives, and digital, analog, and hybrid design approaches. Finally, the primary IMC device problems and metrics are examined and compared.