All models that process megapixel images will use memory very differently than tiny models like ResNet-50’s 224x224. The ratio of weights correspond to activation flips for large images. To really get a sense for how well an inference accelerator will perform for any CNN with megapixel images, a vendor needs to look at a benchmark that uses megapixel images. The clear industry trend is to larger models and larger images so YOLOv3 is more representative of the future of inference acceleration. Using on-chip memory effectively will be critical for low cost/low power inference.
Geoff Tate is the CEO of Flex Logix Technologies Inc. (Mountain View, Calif.), a licensor of FPGA fabric and neural network acceleration cores. Prior to his current position Tate was a co-founder and CEO of Rambus Inc.
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