Papers › Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor Contractions
Yan Ru Pei
We introduce Centaurus, a class of networks composed of generalized state-space model (SSM) blocks, where the SSM operations can be treated as tensor contractions during training. The optimal order of tensor contractions can then be systematically determined for every SSM block to maximize training efficiency. This allows more flexibility in designing SSM blocks beyond the depthwise-separable configuration commonly implemented. The new design choices will take inspiration from classical convolutional blocks including group convolutions, full convolutions, and bottleneck blocks. We architect the Centaurus network with a mixture of these blocks, to balance between network size and performance, as well as memory and computational efficiency during both training and inference. We show that this heterogeneous network design outperforms its homogeneous counterparts in raw audio processing tasks including keyword spotting, speech denoising, and automatic speech recognition (ASR). For ASR, Centaurus is the first network with competitive performance that can be made fully state-space based, without using any nonlinear recurrence (LSTMs), explicit convolutions (CNNs), or (surrogate) attention mechanism. Source code is available at github.com/Brainchip-Inc/Centaurus
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Speech Enhancement | VoiceBank + DEMAND | Centaurus (0.51M) | PESQ (wb) | 3.25 | #19 of 42 | Archive leaderboard | report |
| Speech Recognition | LibriSpeech test-clean | Centaurus (30 M) | Word Error Rate (WER) | 4.4 | #54 of 64 | Archive leaderboard | report |
| Speech Recognition | Speech Commands | Centaurus | Accuracy (%) | 98.53 | #1 of 3 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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