Summary of Imssa: Deploying Modern State-space Models on Memristive In-memory Compute Hardware, by Sebastian Siegel et al.
IMSSA: Deploying modern state-space models on memristive in-memory compute hardware
by Sebastian Siegel, Ming-Jay Yang, John-Paul Strachan
First submitted to arxiv on: 28 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to processing long temporal sequences using structured state-space sequential (S4) models, which offer a fixed memory state while still enabling the processing of very long sequence contexts. The authors aim to bring the power of S4 models to edge hardware by significantly reducing the size and computational demand of an S4D model through quantization-aware training, even achieving ternary weights for a simple real-world task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes deep learning more accessible on edge devices by developing an S4D model that can run efficiently on memory- intensive tasks. The authors use quantization-aware training to reduce the size and computational demand of the model, making it suitable for deployment on edge hardware. |
Keywords
» Artificial intelligence » Deep learning » Quantization
