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Deep learning as a means to inferencing has proliferated thanks to itsversatility and ability to approach or exceed human-level accuracy. Thesecomputational models have seemingly insatiable appetites for computationalresources not only while training, but also when deployed at scales rangingfrom data centers all the way down to embedded devices. As such, increasingconsideration is being made to maximize the computational efficiency givenlimited hardware and energy resources and, as a result, inferencing withreduced precision has emerged as a viable alternative to the IEEE 754 Standardfor Floating-Point Arithmetic. We propose a quantization scheme that allowsinferencing to be carried out using arithmetic that is fundamentally moreefficient when compared to even half-precision floating-point. Our quantizationprocedure is significant in that we determine our quantization schemeparameters by calibrating against its reference floating-point model using asingle inference batch rather than (re)training and achieve end-to-end postquantization accuracies comparable to the reference model.

Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines