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 …

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

This chapter provides approaches to the problem of quantizing the numerical values in deep
Neural Network computations, covering the advantages/disadvantages of current methods …

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

There is a rapidly growing number of large language models (LLMs) that users can query for
a fee. We review the cost associated with querying popular LLM APIs, eg GPT-4, ChatGPT …

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 …

Deep networks run with low precision operations at inference time offer power and space
advantages over high precision alternatives, but need to overcome the challenge of …

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

Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

In this paper, we propose several ideas for enhancing a binary network to close its accuracy
gap from real-valued networks without incurring any additional computational cost. We first …