Proteus

Abstract

This work exploits the tolerance of Deep Neural Networks (DNNs) to reduced precision numerical representations and specifically, their recently demonstrated ability to tolerate representations of different precision per layer while maintaining accuracy. This flexibility enables improvements over conventional DNN implementations that use a single, uniform representation. This work proposes Proteus, which reduces the data traffic and storage footprint needed by DNNs, resulting in reduced energy and improved area efficiency for DNN implementations. Proteus uses a different representation per layer for both the data (neurons) and the weights (synapses) processed by DNNs. Proteus is a layered extension over existing DNN implementations that converts between the numerical representation used by the DNN execution engines and the shorter, layer-specific fixed-point representation used when reading and writing data values to memory be it on-chip buffers or off-chip memory. Proteus uses a novel memory layout for DNN data, enabling a simple, low-cost and low-energy conversion unit.