all AI news
DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training and Inference. (arXiv:2305.07376v1 [cs.AR])
cs.LG updates on arXiv.org arxiv.org
DNNs are one of the most widely used Deep Learning models. The matrix
multiplication operations for DNNs incur significant computational costs and
are bottlenecked by data movement between the memory and the processing
elements. Many specialized accelerators have been proposed to optimize matrix
multiplication operations. One popular idea is to use Processing-in-Memory
where computations are performed by the memory storage element, thereby
reducing the overhead of data movement between processor and memory. However,
most PIM solutions rely either on novel …
accelerator arxiv computational costs data data movement deep learning digital dnn inference matrix matrix multiplication memory operations popular processing the matrix training