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arXiv · 2404.16317

FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor Contraction

Gabriel Kulp·Andrew Ensinger·Lizhong Chen
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Abstract

Tensors play a vital role in machine learning (ML) and often exhibit properties best explored while maintaining high-order. Efficiently performing ML computations requires taking advantage of sparsity, but generalized hardware support is challenging. This paper introduces FLAASH, a flexible and modular accelerator design for sparse tensor contraction that achieves over 25x speedup for a deep learning workload. Our architecture performs sparse high-order tensor contraction by distributing sparse dot products, or portions thereof, to numerous Sparse Dot Product Engines (SDPEs). Memory structure and job distribution can be customized, and we demonstrate a simple approach as a proof of concept. We address the challenges associated with control flow to navigate data structures, high-order representation, and high-sparsity handling. The effectiveness of our approach is demonstrated through various evaluations, showcasing significant speedup as sparsity and order increase.

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Gabriel Kulp, Andrew Ensinger, Lizhong Chen. 2024-04-25. FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor Contraction. https://arxiv.org/abs/2404.16317

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