A New MRAM-based Process In-Memory Accelerator for Efficient Neural Network Training with Floating Point Precision

2 Mar 2020Hongjie WangYang ZhaoChaojian LiYue WangYingyan Lin

The excellent performance of modern deep neural networks (DNNs) comes at an often prohibitive training cost, limiting the rapid development of DNN innovations and raising various environmental concerns. To reduce the dominant data movement cost of training, process in-memory (PIM) has emerged as a promising solution as it alleviates the need to access DNN weights... (read more)

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