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EIE proposed to accelerate pruned and compressed neural networks, exploitingweight sparsity, activation sparsity, and 4-bit weight-sharing in neuralnetwork accelerators. Since published in ISCA'16, it opened a new design spaceto accelerate pruned and sparse neural networks and spawned manyalgorithm-hardware co-designs for model compression and acceleration, both inacademia and commercial AI chips. In retrospect, we review the background ofthis project, summarize the pros and cons, and discuss new opportunities wherepruning, sparsity, and low precision can accelerate emerging deep learningworkloads.

Retrospective: EIE: Efficient Inference Engine on Sparse and Compressed Neural Network