Faster Convolution Inference Through Using Pre-Calculated Lookup Tables
This work addresses the bottleneck of convolution inference speed for deep learning practitioners, though it appears incremental as it builds on existing methods with specific optimizations.
The paper tackles the problem of slow convolution inference by proposing an algorithm that uses pre-calculated lookup tables for low-cardinality activations, resulting in faster inference times and enabling simpler, more effective CNN-specialized hardware.
Low-cardinality activations permit an algorithm based on fetching the inference values from pre-calculated lookup tables instead of calculating them every time. This algorithm can have extensions, some of which offer abilities beyond those of the currently used algorithms. It also allows for a simpler and more effective CNN-specialized hardware.