  Modeling long range dependencies in sequential data is a fundamental step
towards attaining human-level performance in many modalities such as text,
vision, audio and video. While attention-based models are a popular and
effective choice in modeling short-range interactions, their performance on
tasks requiring long range reasoning has been largely inadequate. In an
exciting result, Gu et al. (ICLR 2022) proposed the $\textit{Structured State
Space}$ (S4) architecture delivering large gains over state-of-the-art models
on several long-range tasks across various modalities. The core proposition of
S4 is the parameterization of state matrices via a diagonal plus low rank
structure, allowing efficient computation. In this work, we show that one can
match the performance of S4 even without the low rank correction and thus
assuming the state matrices to be diagonal. Our $\textit{Diagonal State Space}$
(DSS) model matches the performance of S4 on Long Range Arena tasks, speech
classification on Speech Commands dataset, while being conceptually simpler and
straightforward to implement.
