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Bit-Pragmatic Deep Neural Network Computing., , , , and . ICLR (Workshop), OpenReview.net, (2017)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)An improved FPGA-based specific processor for Blokus Duo., , and . FPT, page 366-369. IEEE, (2014)Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning., , , , , , , , , and 3 other author(s). NEWCAS, page 356-360. IEEE, (2018)Loom: Exploiting Weight and Activation Precisions to Accelerate Convolutional Neural Networks., , , and . CoRR, (2017)ShapeShifter: Enabling Fine-Grain Data Width Adaptation in Deep Learning., , , , , , , , , and 1 other author(s). MICRO, page 28-41. ACM, (2019)Bit-Tactical: A Software/Hardware Approach to Exploiting Value and Bit Sparsity in Neural Networks., , , , , , , , and . ASPLOS, page 749-763. ACM, (2019)Atalanta: A Bit is Worth a "Thousand" Tensor Values., , , , , , , and . ASPLOS (2), page 85-102. ACM, (2024)