Sparse Tensor Accelerator

Sparse tensor accelerators aim to efficiently process the large, sparse datasets common in machine learning and scientific computing, focusing on minimizing computational cost and energy consumption. Current research emphasizes algorithmic innovations like tensor decomposition and low-precision arithmetic (e.g., approximating multiplication with addition), alongside hardware co-design to optimize dataflow and exploit sparsity patterns within specialized architectures. These advancements are crucial for accelerating computationally intensive applications such as large language model inference, solving partial differential equations, and high-dimensional density estimation, ultimately enabling faster and more energy-efficient computation across diverse scientific and engineering domains.