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

• URL: http://arxiv.org/abs/2404.16317v1
• Date: Thu, 25 Apr 2024 03:46:53 GMT
• Title: FLAASH: Flexible Accelerator Architecture for Sparse High-Order Tensor Contraction
• Authors: Gabriel Kulp, Andrew Ensinger, Lizhong Chen,
• Abstract summary: This paper introduces FLAASH, a flexible and modular accelerator design for sparse tensor contraction. Our architecture performs sparse high-order tensor contraction by distributing sparse dot products, or portions thereof, to numerous Sparse Dot Product Engines. The effectiveness of our approach is demonstrated through various evaluations, showcasing significant speedup as sparsity and order increase.
• Score: 3.6640504352010885
• License: http://creativecommons.org/licenses/by/4.0/
• 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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