  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.
