Publications (5)
MCHA: A Memory-Centric Hierarchical Architecture for Parallel-Sequential Computing
Daijing Shi, Hongxiao Zhao, Yihan Fu +7
Emerging workloads, such as Multi-Agent Reinforcement Learning (MARL), large-scale neuromorphic computing, and probabilistic graphical models, intrinsically exhibit parallel-sequen…
Non-Binary LDPC Arithmetic Error Correction For Processing-in-Memory
Daijing Shi, Yihang Zhu, Anjunyi Fan +3
Processing-in-memory (PIM) based on emerging devices such as memristors is more vulnerable to noise than traditional memories, due to the physical non-idealities and complex operat…
Probabilistic Compute-in-Memory Design For Efficient Markov Chain Monte Carlo Sampling
Yihan Fu, Daijing Shi, Anjunyi Fan +4
Markov chain Monte Carlo (MCMC) is a widely used sampling method in modern artificial intelligence and probabilistic computing systems. It involves repetitive random number generat…
RAS: A Bit-Exact rANS Accelerator For High-Performance Neural Lossless Compression
Yuchao Qin, Anjunyi Fan, Bonan Yan
Data centers handle vast volumes of data that require efficient lossless compression, yet emerging probabilistic models based methods are often computationally slow. To address thi…
C2C-Explorer: An Exploration Framework for Chip-to-Chip Interconnect Architectures in LLM Cloud Computing Systems
Jiayi Li, Di Wu, Qingxu Li +10
The scaling-up of large language models (LLMs) necessitates computing systems to have multi-processor-chip architectures, elevating the importance of chip-to-chip (C2C) communicati…