The impending end of Moore’s Law and Dennard Scaling has prompted a shift in both industry and academia from general-purpose processors (e.g., CPUs and GPGPUs) toward specialized solutions. However, the diverse hardware requirements of edge computing make it challenging for existing specialized architectures to adapt t...
Wen-Ming Li, Zhihua Fan, Haibin Wu et al.· ACM Transactions on Computer...· 0 citations
Sparse GEneral Matrix Multiplication (SpGEMM) is one of the most vital kernels in massive research domains, including bioinformatics, graph analytics, and machine learning. Moreover, with the prosperity of the Big Data era, nonzero elements in sparse matrices of SpGEMM boost rapidly into the magnitude of billions. Thus...
Ming Dun, Cheng Zhang, Shuhan Song et al.· IEEE Transactions on Paralle...· 0 citations
Structured sparsity is a promising approach to scaling large-language-model (LLM) inference, but existing forms such as butterfly-structured sparse projections and transformations often map inefficiently to GPUs due to deep stage dependencies and limited bulk parallelism. This paper presents MLX, an algorithm–architect...
Haibin Wu, Wenming Li, Zhihua Fan et al.· International Symposium on C...· 0 citations
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