Kunal's ML@B

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EECS | Senior
ude.yelekreb.lm@rasoglanuk

Project Manager for Ternary Pruning on SqueezeNet

https://github.com/kunalgosar | https://www.linkedin.com/in/kunalgosar/

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    Kunal wrote an update for Ternary Weight Pruning for SqueezeNet.

    We have been working on building a generalized method to ternary prune a network, and have been working on testing and benchmarking our approach. We have been surprised by the efficiency and power of ternary pruning and through our benchmarks saw a drop in accuracy from 98.16% to 98.07%, with a 16x reduction in model size. We also saw 83% accuracy on ResNet18 on the Cifar10 Dataset, compared to a baseline score of 77% without Ternary Pruning.