Deep neural networks have been applied in many applications exhibiting extraordinary
abilities in the field of computer vision. However, complex network architectures challenge …

Binary neural networks (BNNs) have received increasing attention due to their superior
reductions of computation and memory. Most existing works focus on either lessening the …

This paper proposes Mandheling, the first system that enables highly resource-efficient on-
device training by orchestrating mixed-precision training with on-chip Digital Signal …

Deep neural network (DNN) training is notoriously time-consuming, and quantization is
promising to improve the training efficiency with reduced bandwidth/storage requirements …

Binary neural networks (BNNs) have attracted broad research interest due to their efficient
storage and computational ability. Nevertheless, a significant challenge of BNNs lies in …

Embodiments of a device include an integrated circuit, a reconfigurable stream switch
formed in the integrated circuit along with a plurality of convolution accelerators and an …

(57) APSTRACT Embodiments of a device include an integrated circuit, a reconfigurable
stream switch formed in the integrated circuit, and an arithmetic unit coupled to the …

Adiabatic Quantum-Flux-Parametron (AQFP) is a superconducting logic with extremely high
energy efficiency. By employing the distinct polarity of current to denote logic '0'and '1', AQFP …

Quantization of the weights and activations is one of the main methods to reduce the
computational footprint of Deep Neural Networks (DNNs) training. Current methods enable 4 …

With the growing demand for deploying Deep Learning models to the “edge”, it is paramount
to develop techniques that allow to execute state-of-the-art models within very tight and …