SISA: A Scale-In Systolic Array for GEMM Acceleration

1. 1Understand the limitations of traditional square Systolic Arrays (SAs) for GEMM operations in LLMs
2. 2Design a scale-in systolic array architecture that can efficiently handle input-dependent and highly skewed matrices
3. 3Implement SISA using Processing Elements (PEs) and evaluate its performance on various AI/ML workloads
4. 4Optimize SISA for specific use cases, such as LLMs and DNNs, to maximize its acceleration benefits

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

💡 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

🚀 SISA: A novel systolic array architecture for accelerating GEMM operations in AI/ML workloads! 🤖