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Massively Parallel Video Networks
[chapter]
2018
Lecture Notes in Computer Science
We introduce a class of causal video understanding models that aims to improve efficiency of video processing by maximising throughput, minimising latency, and reducing the number of clock cycles. Leveraging operation pipelining and multi-rate clocks, these models perform a minimal amount of computation (e.g. as few as four convolutional layers) for each frame per timestep to produce an output. The models are still very deep, with dozens of such operations being performed but in a pipelined
doi:10.1007/978-3-030-01225-0_40
fatcat:bxpmllapyrc7divjz45bkplucq