Algorithm-hardware co-design of adaptive floating-point encodings for resilient deep learning inference

Conventional hardware-friendly quantization methods, such as fixed-point or integer, tend to perform poorly at very low precision as their shrunken dynamic ranges cannot adequately capture the wide data distributions commonly seen in sequence transduction models. We present an algorithm-hardware co-design centered around a novel floating-point inspired number format, AdaptivFloat, that dynamically maximizes and optimally clips its available dynamic range, at a layer granularity, in order to create faithful encodings of neural network parameters. AdaptivFloat consistently produces higher inference accuracies compared to block floating-point, uniform, IEEE-like float or posit encodings at low bit precision (≤ 8-bit) across a diverse set of state-of-the-art neural networks, exhibiting narrow to wide weight distribution. Notably, at 4-bit weight precision, only a 2.1 degradation in BLEU score is observed on the AdaptivFloat-quantized Transformer network compared to total accuracy loss when encoded in the above-mentioned prominent datatypes. Furthermore, experimental results on a deep neural network (DNN) processing element (PE), exploiting AdaptivFloat logic in its computational datapath, demonstrate per-operation energy and area that is 0.9X and 1.14X, respectively, that of an equivalent bit width NVDLA-like integer-based PE.