Deep learning based on neural networks has been widely used in image recognition,
speech recognition, natural language processing, automatic driving, and other fields and …

Large language models (LLMs) face the challenges in fine-tuning and deployment due to
their high memory demands and computational costs. While parameter-efficient fine-tuning …

The nonuniform quantization strategy for compressing neural networks usually achieves
better performance than its counterpart, ie, uniform strategy, due to its superior …

Processing-In-Memory (PIM) accelerators have the potential to efficiently run Deep Neural
Network (DNN) inference by reducing costly data movement and by using resistive RAM …

This work demonstrates a programmable in-memory-computing (IMC) inference accelerator
for scalable execution of neural network (NN) models, leveraging a high-signal-to-noise …

Post-training quantization (PTQ) has been gaining popularity for the deployment of deep
neural networks on resource-limited devices since unlike quantization-aware training …

Quantizing deep neural networks is an effective method for reducing memory consumption
and improving inference speed, and is thus useful for implementation in resource …

In this paper, we propose a fully differentiable quantization method for vision transformer
(ViT) named as Q-ViT, in which both of the quantization scales and bit-widths are learnable …

Deep neural network (DNN) model compression for efficient on-device inference is
becoming increasingly important to reduce memory requirements and keep user data on …

Abstract Model quantization is an important mechanism for energy-efficient deployment of
deep neural networks on resource-constrained devices by reducing the bit precision of …