Sorbet: A neuromorphic hardware-compatible transformer-based spiking model
The paper presents Sorbet, a neuromorphic transformer-based language model focused on energy efficiency for resource-constrained environments, utilizing innovative techniques like PTsoftmax and BSPN to enhance performance and reduce energy consumption.
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The paper titled "Sorbet: A Neuromorphic Hardware-Compatible Transformer-Based Spiking Language Model" introduces a new language model designed for deployment in resource-constrained environments, focusing on energy efficiency. The authors, Kaiwen Tang, Zhanglu Yan, and Weng-Fai Wong, highlight the challenges of implementing key operations like softmax and layer normalization in spiking neural networks (SNNs), which are essential for transformer-based models. To overcome these issues, Sorbet employs a novel shifting-based softmax method called PTsoftmax and a power normalization technique using bit-shifting (BSPN). These innovations aim to replace traditional energy-intensive operations. The model also utilizes knowledge distillation and model quantization to create a highly compressed binary weight model that retains competitive performance while significantly reducing energy consumption. The effectiveness of Sorbet is validated through extensive testing on the GLUE benchmark and various ablation studies, showcasing its potential as an energy-efficient solution for language model inference.
- Sorbet is designed for resource-constrained devices, emphasizing energy efficiency.
- It introduces PTsoftmax and BSPN to address challenges in implementing softmax and layer normalization on SNNs.
- The model achieves a highly compressed binary weight format through knowledge distillation and quantization.
- Extensive testing on the GLUE benchmark demonstrates Sorbet's competitive performance.
- The research highlights the potential of neuromorphic hardware for language model applications.
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6 commentsIn particular the connection between the typical weighted-sum plus activation function and a simplistic spiking model where one considers the output simply by the spiking rate was illuminating (section 3).
[1]: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9313413/ Spiking Neural Networks and Their Applications: A Review
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