A fully binarized Conv3D-LSTM model for video inference runs gesture recognition on Jester with 1.01 MB weights and 6.34 GBOPs, with an 8-9 point accuracy drop versus compact full-precision baselines.
Gating re- visited: Deep multi-layer RNNs that can be trained,
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BILLNET: A Binarized Conv3D-LSTM Network with Logic-gated residual architecture for hardware-efficient video inference
A fully binarized Conv3D-LSTM model for video inference runs gesture recognition on Jester with 1.01 MB weights and 6.34 GBOPs, with an 8-9 point accuracy drop versus compact full-precision baselines.