Mlperf training benchmark
Abstract Machine learning is experiencing an explosion of software and hardware solutions,
and needs industry-standard performance benchmarks to drive design and enable …
and needs industry-standard performance benchmarks to drive design and enable …
Smartsage: training large-scale graph neural networks using in-storage processing architectures
Graph neural networks (GNNs) can extract features by learning both the representation of
each objects (ie, graph nodes) and the relationship across different objects (ie, the edges …
each objects (ie, graph nodes) and the relationship across different objects (ie, the edges …
McDRAM v2: In-dynamic random access memory systolic array accelerator to address the large model problem in deep neural networks on the edge
The energy efficiency of accelerating hundreds of MB-large deep neural networks (DNNs) in
a mobile environment is less than that of a server-class big chip accelerator because of the …
a mobile environment is less than that of a server-class big chip accelerator because of the …
Develo** a recommendation benchmark for mlperf training and inference
Deep learning-based recommendation models are used pervasively and broadly, for
example, to recommend movies, products, or other information most relevant to users, in …
example, to recommend movies, products, or other information most relevant to users, in …
Understanding the implication of non-volatile memory for large-scale graph neural network training
Graph neural networks (GNNs) can extract features by learning both the representation of
each objects (ie, graph nodes) as well as the relationship across different objects (ie, the …
each objects (ie, graph nodes) as well as the relationship across different objects (ie, the …