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Accelerating Deep Learning Inference via Learned Caches
We present the design of GATI, an end-to-end prediction serving system that incorporates learned caches for low-latency DNN inference.
Accelerating Deep Learning Inference via Freezing
In this work, we observe that caching intermediate layer outputs can help us avoid running all the layers of a DNN for a sizeable fraction of inference requests.
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