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Value-Based Deep-Learning Acceleration., , , , , , , and . IEEE Micro, 38 (1): 41-55 (2018)Cnvlutin: Ineffectual-Neuron-Free Deep Neural Network Computing., , , , , and . ISCA, page 1-13. IEEE Computer Society, (2016)MemcachedGPU: scaling-up scale-out key-value stores., , and . SoCC, page 43-57. ACM, (2015)Identifying and Exploiting Ineffectual Computations to Enable Hardware Acceleration of Deep Learning., , , , , , , , , and 3 other author(s). NEWCAS, page 356-360. IEEE, (2018)Proteus: Exploiting precision variability in deep neural networks., , , , , , and . Parallel Comput., (2018)Characterizing and evaluating a key-value store application on heterogeneous CPU-GPU systems., , , , and . ISPASS, page 88-98. IEEE Computer Society, (2012)Proteus: Exploiting Numerical Precision Variability in Deep Neural Networks., , , , , and . ICS, page 23:1-23:12. ACM, (2016)GPUWattch: enabling energy optimizations in GPGPUs., , , , , , and . ISCA, page 487-498. ACM, (2013)Stripes: Bit-serial deep neural network computing., , , , and . MICRO, page 19:1-19:12. IEEE Computer Society, (2016)Reduced-Precision Strategies for Bounded Memory in Deep Neural Nets., , , , , , and . CoRR, (2015)