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

arXiv:2404.16317v1 Announce Type: cross
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 …

abstract accelerator architecture arxiv cs.ar cs.lg design generalized hardware machine machine learning modular paper role sparsity support tensor type vital while