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Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How, , , , , , , and . (2018)cite arxiv:1803.03688Comment: An earlier version of this work titled "JaZ: Enabling Innovation Towards Chaff-Free Deep Learning Computing" was submitted for blind review.Dynamic Stripes: Exploiting the Dynamic Precision Requirements of Activation Values in Neural Networks., , , and . CoRR, (2017)Bit-pragmatic deep neural network computing., , , , , , and . MICRO, page 382-394. ACM, (2017)Boveda: Building an On-Chip Deep Learning Memory Hierarchy Brick by Brick., , , , , , , , , and . MLSys, mlsys.org, (2021)Loom: exploiting weight and activation precisions to accelerate convolutional neural networks., , , , and . DAC, page 20:1-20:6. ACM, (2018)Laconic deep learning inference acceleration., , , , , , , and . ISCA, page 304-317. ACM, (2019)Late Breaking Results: Building an On-Chip Deep Learning Memory Hierarchy Brick by Brick., , , , , , and . DAC, page 1-2. IEEE, (2020)Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning., , , , , , , , , and 3 other author(s). NEWCAS, page 356-360. IEEE, (2018)Bit-pragmatic Deep Neural Network Computing., , , , and . CoRR, (2016)Bit-Pragmatic Deep Neural Network Computing., , , , and . ICLR (Workshop), OpenReview.net, (2017)