TensorDIMM moves embedding gathers and reductions into the DRAM buffer chip and pools such DIMMs in the GPU interconnect, reporting large speedups on recommender inference.
Competitive Analysis System for Theatrical Movie Releases Based on Movie Trailer Deep Video Representation
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abstract
Audience discovery is an important activity at major movie studios. Deep models that use convolutional networks to extract frame-by-frame features of a movie trailer and represent it in a form that is suitable for prediction are now possible thanks to the availability of pre-built feature extractors trained on large image datasets. Using these pre-built feature extractors, we are able to process hundreds of publicly available movie trailers, extract frame-by-frame low level features (e.g., a face, an object, etc) and create video-level representations. We use the video-level representations to train a hybrid Collaborative Filtering model that combines video features with historical movie attendance records. The trained model not only makes accurate attendance and audience prediction for existing movies, but also successfully profiles new movies six to eight months prior to their release.
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cs.LG 1years
2019 1verdicts
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TensorDIMM: A Practical Near-Memory Processing Architecture for Embeddings and Tensor Operations in Deep Learning
TensorDIMM moves embedding gathers and reductions into the DRAM buffer chip and pools such DIMMs in the GPU interconnect, reporting large speedups on recommender inference.