@conference {Tambe2020,
	title = {Algorithm-Hardware Co-Design of Adaptive Floating-Point Encodings for Resilient Deep Learning Inference},
	year = {2020},
	note = {Best paper award},
	publisher = {Design Automation Conference (DAC 2020)},
	organization = {Design Automation Conference (DAC 2020)},
	address = {San Francisco, CA, USA},
	abstract = {Conventional hardware-friendly quantization methods, such asfixed-point or integer, tend to perform poorly at very low preci-sion as their shrunken dynamic ranges cannot adequately capturethe wide data distributions commonly seen in sequence transduc-tion models. We present an algorithm-hardware co-design centeredaround a novel floating-point inspired number format,AdaptivFloat,that dynamically maximizes and optimally clips its available dy-namic range, at a layer granularity, in order to create faithful encod-ings of neural network parameters. AdaptivFloat consistently pro-duces 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, ex-hibiting narrow to wide weight distribution. Notably, at 4-bit weightprecision, only a 2.1 degradation in BLEU score is observed on theAdaptivFloat-quantized Transformer network compared to totalaccuracy loss when encoded in the above-mentioned prominentdatatypes. Furthermore, experimental results on a deep neural net-work (DNN) processing element (PE), exploiting AdaptivFloat logicin its computational datapath, demonstrate per-operation energyand area that is 0.9{\texttimes}and 1.14{\texttimes}, respectively, that of an equivalentbit width NVDLA-like integer-based PE.},
	keywords = {deep learning, dnn, hardware support for machine learning},
	url = {https://doi.org/10.1109/DAC18072.2020.9218516},
	author = {Tambe, Thierry and En-Yang and Wan, Zishen and Deng, Yuntian and Vijay Reddi and Rush, Alexander and David Brooks and Gu-Yeon Wei}
}
