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HP Inc.,Palo Alto,California,USA, HP Inc., Palo Alto, California, USA
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Reducing ResNet-50's computational load by nearly 50% while boosting accuracy demonstrates a breakthrough in deploying CNNs on embedded devices.
Achieving real-time performance on edge devices, this method reduces GoogLeNet's latency from 40.32 ms to 34 ms with minimal accuracy loss, while VGG-19 sees a latency drop from 119.98 ms to 34 ms with an accuracy gain.
Smart Scissor reduces CNN computational costs by over 40% while actually improving accuracy, challenging the notion that efficiency comes at the expense of performance.
Heterogeneous models can be collaboratively trained to improve accuracy under strict latency constraints, achieving significant performance gains without extra training costs.
