SISA: A Scale-In Systolic Array for GEMM Acceleration
The currently dominant AI/ML workloads, such as Large Language Models (LLMs), rely on the efficient execution of General Matrix-Matrix Multiplication (GEMM) operations. Thus, most systems are equipped with dedicated matrix hardware accelerators based on square Systolic Arrays (SAs) of Processing Elements (PEs). While this organization was effective for traditional Deep Neural Networks (DNNs), LLMs introduce input-dependent and highly skewed matrices, leading to underutilized SA resources. To address this challenge, we propose SISA (Scale-In Systolic Array), a novel SA architecture that partitions the traditional square array into horizontal rectangular slabs. With minimal overhead, SISA e...
| Authors | Luigi Altamura, Alessio Cicero, Mateo Vázquez Maceiras et al. |
|---|---|
| Primary category | cs.AR |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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