Transporter: A 128$\times$4 SPAD Imager with On-chip Encoder for Spiking Neural Network-based Processing
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917067543805952 |
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| author | Lin, Yang Bruschini, Claudio Charbon, Edoardo |
| author_facet | Lin, Yang Bruschini, Claudio Charbon, Edoardo |
| contents | Single-photon avalanche diodes (SPADs) are widely used today in time-resolved imaging applications. However, traditional architectures rely on time-to-digital converters (TDCs) and histogram-based processing, leading to significant data transfer and processing challenges. Previous work based on recurrent neural networks has realized histogram-free processing. To further address these limitations, we propose a novel paradigm that eliminates TDCs by integrating in-sensor spike encoders. This approach enables preprocessing of photon arrival events in the sensor while significantly compressing data, reducing complexity, and maintaining real-time edge processing capabilities. A dedicated spike encoder folds multiple laser repetition periods, transforming phase-based spike trains into density-based spike trains optimized for spiking neural network processing and training via backpropagation through time. As a proof of concept, we introduce Transporter, a 128$\times$4 SPAD sensor with a per-pixel D flip-flop ring-based spike encoder, designed for intelligent active time-resolved imaging. This work demonstrates a path toward more efficient, neuromorphic SPAD imaging systems with reduced data overhead and enhanced real-time processing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05241 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transporter: A 128$\times$4 SPAD Imager with On-chip Encoder for Spiking Neural Network-based Processing Lin, Yang Bruschini, Claudio Charbon, Edoardo Image and Video Processing Single-photon avalanche diodes (SPADs) are widely used today in time-resolved imaging applications. However, traditional architectures rely on time-to-digital converters (TDCs) and histogram-based processing, leading to significant data transfer and processing challenges. Previous work based on recurrent neural networks has realized histogram-free processing. To further address these limitations, we propose a novel paradigm that eliminates TDCs by integrating in-sensor spike encoders. This approach enables preprocessing of photon arrival events in the sensor while significantly compressing data, reducing complexity, and maintaining real-time edge processing capabilities. A dedicated spike encoder folds multiple laser repetition periods, transforming phase-based spike trains into density-based spike trains optimized for spiking neural network processing and training via backpropagation through time. As a proof of concept, we introduce Transporter, a 128$\times$4 SPAD sensor with a per-pixel D flip-flop ring-based spike encoder, designed for intelligent active time-resolved imaging. This work demonstrates a path toward more efficient, neuromorphic SPAD imaging systems with reduced data overhead and enhanced real-time processing. |
| title | Transporter: A 128$\times$4 SPAD Imager with On-chip Encoder for Spiking Neural Network-based Processing |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2511.05241 |