  Transformers have widely adopted attention networks for sequence mixing and
MLPs for channel mixing, playing a pivotal role in achieving breakthroughs
across domains. However, recent literature highlights issues with attention
networks, including low inductive bias and quadratic complexity concerning
input sequence length. State Space Models (SSMs) like S4 and others (Hippo,
Global Convolutions, liquid S4, LRU, Mega, and Mamba), have emerged to address
the above issues to help handle longer sequence lengths. Mamba, while being the
state-of-the-art SSM, has a stability issue when scaled to large networks for
computer vision datasets. We propose SiMBA, a new architecture that introduces
Einstein FFT (EinFFT) for channel modeling by specific eigenvalue computations
and uses the Mamba block for sequence modeling. Extensive performance studies
across image and time-series benchmarks demonstrate that SiMBA outperforms
existing SSMs, bridging the performance gap with state-of-the-art transformers.
Notably, SiMBA establishes itself as the new state-of-the-art SSM on ImageNet
and transfer learning benchmarks such as Stanford Car and Flower as well as
task learning benchmarks as well as seven time series benchmark datasets. The
project page is available on this website
~\url{https://github.com/badripatro/Simba}.
