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Cost-Driven Hardware-Software Co-Optimization of Machine Learning Pipelines
We perform this hardware/software co-design from the cost, latency, and user-experience perspective, and develop a set of guidelines for optimal system design and model deployment for the most cost-constrained platforms.
SWIS -- Shared Weight bIt Sparsity for Efficient Neural Network Acceleration
Quantization is spearheading the increase in performance and efficiency of neural network computing systems making headway into commodity hardware.
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