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 …

Abstract Design automation is a core technology in industrial design software and an
important branch of knowledge-worker automation. For example, electronic design …

Applying a pre-trained large model to downstream tasks is prohibitive under resource-
constrained conditions. Recent dominant approaches for addressing efficiency issues …

Purpose For privacy protection, federated learning based on data separation allows
machine learning models to be trained on remote devices or in isolated data devices …

Quantization, effective Neural Network architecture, and efficient accelerator hardware are
three important design paradigms to maximize accuracy and efficiency. Mixed Precision …

The scaling laws have become the de facto guidelines for designing large language models
(LLMs), but they were studied under the assumption of unlimited computing resources for …

Compressing large neural networks with minimal performance loss is crucial to enabling
their deployment on edge devices.(Cho et al., 2022) proposed a weight quantization method …

Field-aware Factorization Machines (FFMs) have emerged as a powerful model for click-
through rate prediction, particularly excelling in capturing complex feature interactions. In …

Quantization is commonly used in Deep Neural Networks (DNNs) to reduce the storage and
computational complexity by decreasing the arithmetical precision of activations and …

In recent years, deep neural networks have achieved tremendous success in image
classification in both academic and industrial settings. However, the high hardware …