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Conventional hardware-friendly quantization methods, such as fixed-point orinteger, tend to perform poorly at very low word sizes as their shrinkingdynamic ranges cannot adequately capture the wide data distributions commonlyseen in sequence transduction models. We present AdaptivFloat, a floating-pointinspired number representation format for deep learning that dynamicallymaximizes and optimally clips its available dynamic range, at a layergranularity, in order to create faithful encoding of neural network parameters.AdaptivFloat consistently produces higher inference accuracies compared toblock floating-point, uniform, IEEE-like float or posit encodings at very lowprecision ($\leq$ 8-bit) across a diverse set of state-of-the-art neuralnetwork topologies. And notably, AdaptivFloat is seen surpassing baseline FP32performance by up to +0.3 in BLEU score and -0.75 in word error rate at weightbit widths that are $\leq$ 8-bit. Experimental results on a deep neural network(DNN) hardware accelerator, exploiting AdaptivFloat logic in its computationaldatapath, demonstrate per-operation energy and area that is 0.9$\times$ and1.14$\times$, respectively, that of equivalent bit width integer-basedaccelerator variants.

AdaptivFloat: A Floating-point based Data Type for Resilient Deep Learning Inference