Hardware-Software Co-Design of Scalable, Energy-Efficient Analog Recurrent Computations
Always-on AI applications, from environmental sensors to biomedical implants, require ultra-low power consumption. Analog circuits offer a path to sub-microwatt inference, yet existing analog implementations are limited to feedforward architectures: extending them to recurrent dynamics has been considered impractical due to noise accumulation through temporal feedback. We demonstrate that this barrier can be overcome through hardware-software co-design. Specifically, we identify that Bistable Memory Recurrent Units (BMRUs), a class of Recurrent Neural Networks (RNNs) with discrete-valued outputs and hysteretic dynamics, admit an ultra-low power current-mode analog implementation which we ...
| Authors | Arthur Fyon, Julien Brandoit, Loris Mendolia et al. |
|---|---|
| Primary category | cs.AR |
| Published | 12 May 2026 |
| Last updated at source | 26 May 2026 |
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