Domain-specific hardware is becoming a promising topic in the backdrop of improvement
slow down for general-purpose processors due to the foreseeable end of Moore's Law …

Spiking neural networks (SNNs) are inspired by information processing in biology, where
sparse and asynchronous binary signals are communicated and processed in a massively …

The strength of modern generative models lies in their ability to be controlled through
prompts. Hard prompts comprise interpretable words and tokens, and are typically hand …

The binary neural network, largely saving the storage and computation, serves as a
promising technique for deploying deep models on resource-limited devices. However, the …

Hardware-friendly network quantization (eg, binary/uniform quantization) can efficiently
accelerate the inference and meanwhile reduce memory consumption of the deep neural …

Communication bottleneck has been identified as a significant issue in distributed
optimization of large-scale learning models. Recently, several approaches to mitigate this …

Very deep convolutional neural networks offer excellent recognition results, yet their
computational expense limits their impact for many real-world applications. We introduce …

Researches on deep neural networks with discrete parameters and their deployment in
embedded systems have been active and promising topics. Although previous works have …

In this paper, we propose a number of novel techniques and numerical representation
formats that enable, for the very first time, the precision of training systems to be aggressively …

Deep neural networks are the state-of-the-art methods for many real-world tasks, such as
computer vision, natural language processing and speech recognition. For all its popularity …