A novel high-fan-in differential superconductor neuron structure designed for ultra-high-
performance spiking neural network (SNN) accelerators is presented. Utilizing a high-fan-in …

Deep Learning has been one of the most disruptive technological advancements in recent
times. The high performance of deep learning models comes at the expense of high …

Quantization is a widespread method for reducing the inference time of neural networks on
mobile Central Processing Units (CPUs). Eight-bit quantized networks demonstrate similarly …

The advancements of hardware technology in recent years has brought many possibilities
for low-precision applications. However, the use of low precision can introduce significant …

This survey presents a comprehensive analysis of the potential benefits and challenges of
training deep neural networks (DNNs) on CPUs, summarizing existing research in the field …

(English) Over the past decade, significant progresses in the field of artificial intelligence
have led to remarkable advancements in a wide range of technologies. Deep learning, a …

A system, method, and computer readable medium for deploying neural networks in low bit
environments. The system comprises a runtime platform, a first set of configuration …