arXiv subjects

Hanyuan Gao

Publications and source records attributed to Hanyuan Gao.

2 recordsLinked to original sources

arXiv2026

Area-Efficient In-Memory Computing for Mixture-of-Experts via Multiplexing and Caching

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 (PIM) architectures, because PIM can naturally fit experts separately and provide great benefits for energy efficiency. However, PIM chips often suffer from large area overhead, especially in the peripheral circuits. In this paper, we propose an area-efficient in-memory computing architecture for MoE transformers. First, to reduce area, we propose a crossbar-level multiplexing strategy that exploits MoE sparsity: experts are deployed on crossbars and multiple crossbars share the same peripheral circuits. Second, we propose expert grouping and group-wise scheduling methods to alleviate the load imbalance and contention overhead caused by sharing. In addition, to address the problem that the expert choice router requires access to all hidden states during generation, we propose a gate-output (GO)cache to store necessary results and bypass expensive additional computation. Experiments show that our approaches improve the area efficiency of the MoE part by up to 2.2x compared to a SOTA architecture. During generation, the cache improves performance and energy efficiency by 4.2x and 10.1x, respectively, compared to the baseline when generating 8 tokens. The total performance density achieves 15.6 GOPS/W/mm2. The code is open source at https://github.com/superstarghy/MoEwithPIM.

cs.AR↗

arXiv2025

Norm-Q: Effective Compression Method for Hidden Markov Models in Neuro-Symbolic Applications

Hidden Markov models (HMM) are commonly used in generation tasks and have demonstrated strong capabilities in neuro-symbolic applications for the Markov property. These applications leverage the strengths of neural networks and symbolic reasoning to create robust and interpretable AI systems. However, they may inherit and amplify the shortcomings of both approaches. Both components require dense computation and data transfer, and their communication further hinders performance. This paper proposes Norm-Q, a normalized linear quantization approach for compressing probabilistic symbolic models, such as HMMs. We reduce the bit width of the data with minimal impact, thereby alleviating memory and bandwidth stress and enabling deployment on potential custom hardware. Our method introduces a normalized quantization-aware expectation maximization process for probabilistic model training. The experimental results show that Norm-Q achieves a higher compression rate with reasonable score loss compared to traditional quantization methods. In the case of the constrained generation task of large language models, we successfully quantize an HMM of 4096 hidden states to 8 bits without loss and, at most, 3 bits with acceptable loss. Notably, the Norm-Q method can achieve a compression rate of 99% for the weights of the HMM. The code is open source at https://github.com/superstarghy/Norm-Q.

cs.LG↗