9 citations · 19 across the 15 of their papers we have counts for
15 papers
Lonic: Algorithm-Hardware Co-Design for Energy-Efficient Fully Local Online SNN Training with INT4 Precision
Peilin Chen, Xiaoxuan Yang
Spiking neural networks (SNNs) have recently attracted increasing attention as an energy-efficient learning paradigm. Existing works also propose temporally and fully local online…
SpikON: A Dual-Parallel and Efficient Accelerator for Online Spiking Neural Networks Learning
Peilin Chen, Xiaoxuan Yang
Spiking neural networks (SNNs) have emerged as a promising paradigm for energy-efficient brain-inspired computing. However, existing online unsupervised SNN learning suffers from l…
Area-Efficient In-Memory Computing for Mixture-of-Experts via Multiplexing and Caching
Hanyuan Gao, Xiaoxuan Yang
Mixture-of-Experts (MoE) layers activate a subset of model weights, dubbed experts, to improve model performance. MoE is particularly promising for deployment on process-in-memory…
End-to-End Transformer Acceleration Through Processing-in-Memory Architectures
Xiaoxuan Yang, Peilin Chen, Tergel Molom-Ochir +1
Transformers have become central to natural language processing and large language models, but their deployment at scale faces three major challenges. First, the attention mechanis…
Norm-Q: Effective Compression Method for Hidden Markov Models in Neuro-Symbolic Applications
Hanyuan Gao, Xiaoxuan Yang
Hidden Markov models (HMM) are commonly used in generation tasks and have demonstrated strong capabilities in neuro-symbolic applications for the Markov property. These application…
Titanus: Enabling KV Cache Pruning and Quantization On-the-Fly for LLM Acceleration
Peilin Chen, Xiaoxuan Yang
Large language models (LLMs) have gained great success in various domains. Existing systems cache Key and Value within the attention block to avoid redundant computations. However,…
AutoRAC: Automated Processing-in-Memory Accelerator Design for Recommender Systems
Feng Cheng, Tunhou Zhang, Junyao Zhang +6
The performance bottleneck of deep-learning-based recommender systems resides in their backbone Deep Neural Networks. By integrating Processing-In-Memory~(PIM) architectures, resea…
Optimizing and Exploring System Performance in Compact Processing-in-Memory-based Chips
Peilin Chen, Xiaoxuan Yang
Processing-in-memory (PIM) is a promising computing paradigm to tackle the "memory wall" challenge. However, PIM system-level benefits over traditional von Neumann architecture can…
Memory Is All You Need: An Overview of Compute-in-Memory Architectures for Accelerating Large Language Model Inference
Christopher Wolters, Xiaoxuan Yang, Ulf Schlichtmann +1
Large language models (LLMs) have recently transformed natural language processing, enabling machines to generate human-like text and engage in meaningful conversations. This devel…
Block-Wise Mixed-Precision Quantization: Enabling High Efficiency for Practical ReRAM-based DNN Accelerators
Xueying Wu, Edward Hanson, Nansu Wang +9
Resistive random access memory (ReRAM)-based processing-in-memory (PIM) architectures have demonstrated great potential to accelerate Deep Neural Network (DNN) training/inference.…
Biologically Plausible Learning on Neuromorphic Hardware Architectures
Christopher Wolters, Brady Taylor, Edward Hanson +3
With an ever-growing number of parameters defining increasingly complex networks, Deep Learning has led to several breakthroughs surpassing human performance. As a result, data mov…
Approximate Computing and the Efficient Machine Learning Expedition
Jörg Henkel, Hai Li, Anand Raghunathan +4
Approximate computing (AxC) has been long accepted as a design alternative for efficient system implementation at the cost of relaxed accuracy requirements. Despite the AxC researc…
HERO: Hessian-Enhanced Robust Optimization for Unifying and Improving Generalization and Quantization Performance
Huanrui Yang, Xiaoxuan Yang, Neil Zhenqiang Gong +1
With the recent demand of deploying neural network models on mobile and edge devices, it is desired to improve the model's generalizability on unseen testing data, as well as enhan…
Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise
Xiaoxuan Yang, Syrine Belakaria, Biresh Kumar Joardar +5
Resistive random-access memory (ReRAM) is a promising technology for designing hardware accelerators for deep neural network (DNN) inferencing. However, stochastic noise in ReRAM c…
Harnessing Optoelectronic Noises in a Photonic Generative Network
Changming Wu, Xiaoxuan Yang, Heshan Yu +4
Integrated optoelectronics is emerging as a promising platform of neural network accelerator, which affords efficient in-memory computing and high bandwidth interconnectivity. The…