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SMASH: Co-Designing Sparse Matrix Hardware Accelerators

Explore software compression and hardware-accelerated indexing for efficient sparse matrix operations in diverse applications like neural networks and recommender systems. Learn about SMASH, a cooperative mechanism achieving significant speedups and minimal hardware overhead.

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SMASH: Co-Designing Sparse Matrix Hardware Accelerators

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  1. Session V-A: Domain Specific Accelerators (Tuesday 1:30PM) SMASH Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations Konstantinos Kanellopoulos, Nandita Vijaykumar, Christina Giannoula, Roknoddin Azizi, Skanda Koppula, Nika Mansouri Ghiasi, Taha Shahroodi, Juan Gomez Luna, OnurMutlu

  2. Sparse Matrix Operations are Widespread Today Neural Networks Recommender Systems Graph Analytics • Graph Neural Networks • Sparse Deep • Neural Networks • PageRank • Breadth-First Search • Betweenness • Centrality • Collaborative Filtering Sparse matrix compression is essential to enable efficient storage and computation 1

  3. Limitations of Existing Compression Formats 1 Expensive discovery of the positions of non-zero elements General formats optimize for storage 2 Specialized formats assume specific matrix structures and patterns (e.g., diagonals) Narrow applicability 2

  4. SMASH • Hardware/Software cooperative mechanism: • Enables highly-efficient sparse matrix compression and computation • General across a diverse set of sparse matrices and sparse matrix operations Software Hardware Efficient compression using a Hierarchy of Bitmaps Unit that scans bitmaps to accelerate indexing SMASH ISA 3

  5. Key Results SMASH • 38% and 44% speedup for SpMV and SpMM Hardware Overhead • 0.076% area overhead over an Intel Xeon CPU 4

  6. Session V-A: Domain Specific Accelerators (Tuesday 1:30PM) SMASH Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations Konstantinos Kanellopoulos, Nandita Vijaykumar, Christina Giannoula, Roknoddin Azizi, Skanda Koppula, Nika Mansouri Ghiasi, Taha Shahroodi, Juan Gomez Luna, OnurMutlu

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