8 papers
Spontaneous Scoto-leptogenesis
Arghyajit Datta, Hyun Min Lee, Jun-Ho Song
We propose a low-scale spontaneous leptogenesis scenario within the dynamical minimal scotogenic model for accommodating neutrino masses and inert scalar dark matter simultaneously…
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,…