Abstract
Current study results on Machine Learn ing have been published. According to news reporting originating in Shanghai, Pe ople's Republic of China, by NewsRx journalists, research stated, "Processing-in -Memory (PIM) has been widely explored for accelerating data-intensive machine l earning computation that mainly consists of general-matrix-multiplication (GEMM) , by mitigating the burden of data movements and exploiting the ultra-high memor y parallelism. The two mainstreams of PIM, the analog- and digital-type, have bo th been exploited in accelerating machine learning workloads by numerous outstan ding prior works." Financial supporters for this research include National Natural Science Foundati on of China (NSFC), National Key Research & Development Program of China, MSRA gift fund, Lingang Laboratory Open Research Fund, SJTU-BIREN.
NETL
NSTL