The research advances concerning the typical architectures of convolutional neural
networks (CNNs) as well as their optimizations are analyzed and elaborated in detail in this …

Neural information retrieval (IR) has greatly advanced search and other knowledge-
intensive language tasks. While many neural IR methods encode queries and documents …

For autoregressive (AR) modeling of high-resolution images, vector quantization (VQ)
represents an image as a sequence of discrete codes. A short sequence length is important …

We introduce MoMask a novel masked modeling framework for text-driven 3D human
motion generation. In MoMask a hierarchical quantization scheme is employed to represent …

In the electronics industry, binary devices have played a critical role since the development
of solid-state transistors. While binary technology associates devices' inherent ability to be …

Deep neural networks (DNNs) have achieved great success in many object detection tasks.
However, such DNNS-based large object detection models are generally computationally …

Ternary Neural Networks (TNNs) compress network weights and activation functions into 2-
bit representation resulting in remarkable network compression and energy efficiency …

Low-precision quantized neural networks (QNNs) reduce the required memory space,
bandwidth, and computational power, and hence are suitable for deployment in applications …

This paper investigates a runtime accuracy reconfigurable implementation of an energy
efficient deep learning accelerator. It is based on voltage overscaling (VOS) technique which …

Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …