5 papers · 1 filter
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…
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…
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,…
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…