SISA: A Scale-In Systolic Array for GEMM Acceleration
📰 ArXiv cs.AI
SISA is a novel systolic array architecture for accelerating General Matrix-Matrix Multiplication (GEMM) operations in AI/ML workloads
Action Steps
- Understand the limitations of traditional square Systolic Arrays (SAs) for GEMM operations in LLMs
- Design a scale-in systolic array architecture that can efficiently handle input-dependent and highly skewed matrices
- Implement SISA using Processing Elements (PEs) and evaluate its performance on various AI/ML workloads
- Optimize SISA for specific use cases, such as LLMs and DNNs, to maximize its acceleration benefits
Who Needs to Know This
AI engineers and researchers working on Large Language Models (LLMs) and Deep Neural Networks (DNNs) can benefit from SISA's efficient GEMM acceleration, enabling them to improve model performance and reduce computational costs
Key Insight
💡 SISA's scale-in design enables more efficient execution of GEMM operations in LLMs and DNNs, leading to improved model performance and reduced computational costs
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Key Takeaways
SISA is a novel systolic array architecture for accelerating General Matrix-Matrix Multiplication (GEMM) operations in AI/ML workloads
Full Article
Title: SISA: A Scale-In Systolic Array for GEMM Acceleration
Abstract:
arXiv:2603.29913v1 Announce Type: cross Abstract: 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 matri
Abstract:
arXiv:2603.29913v1 Announce Type: cross Abstract: 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 matri
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