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Loom: exploiting weight and activation precisions to accelerate convolutional neural networks.

, , , , and . DAC, page 20:1-20:6. ACM, (2018)

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Loom: exploiting weight and activation precisions to accelerate convolutional neural networks., , , , and . DAC, page 20:1-20:6. ACM, (2018)Tartan: Accelerating Fully-Connected and Convolutional Layers in Deep Learning Networks by Exploiting Numerical Precision Variability., , , and . CoRR, (2017)FP8 Formats for Deep Learning., , , , , , , , , and 5 other author(s). CoRR, (2022)DPRed: Making Typical Activation Values Matter In Deep Learning Computing., , , , and . CoRR, (2018)Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning., , , , , , , , , and 3 other author(s). NEWCAS, page 356-360. IEEE, (2018)Evaluating the memory system behavior of smartphone workloads., , , , , , , , , and 1 other author(s). ICSAMOS, page 83-92. IEEE, (2014)Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets., , , , , , and . CoRR, (2015)Bit-Tactical: Exploiting Ineffectual Computations in Convolutional Neural Networks: Which, Why, and How., , , , , , , and . CoRR, (2018)Exploiting Typical Values to Accelerate Deep Learning., , , , , , , , and . Computer, 51 (5): 18-30 (2018)Bit-pragmatic Deep Neural Network Computing., , , , and . CoRR, (2016)