3-D In-Sensor Computing for Real-Time DVS Data Compression: 65-nm Hardware-Algorithm Co-Design
Traditional IO links are insufficient to transport high volume of image sensor data, under stringent power and latency constraints. To address this, we demonstrate a low latency, low power in-sensor computing architecture to compress the data from a 3D-stacked dynamic vision sensor (DVS). In this de...
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| Vydané v: | IEEE solid-state circuits letters Ročník 7; s. 119 - 122 |
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| Hlavní autori: | , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Piscataway
IEEE
2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Predmet: | |
| ISSN: | 2573-9603, 2573-9603 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Traditional IO links are insufficient to transport high volume of image sensor data, under stringent power and latency constraints. To address this, we demonstrate a low latency, low power in-sensor computing architecture to compress the data from a 3D-stacked dynamic vision sensor (DVS). In this design, we adopt a 4-bit autoencoder algorithm and implement it on an AI computing layer with in-memory computing (IMC) to enable real-time compression of DVS data. To support 3-D integration, this architecture is optimized to handle the unique constraints, including footprint to match the size of the sensor array, low latency to manage the continuous data stream, and low-power consumption to avoid thermal issues. Our prototype chip in 65-nm CMOS demonstrates the new concept of 3-D in-sensor computing, achieving < 6 mW power consumption at 1-10 MHz operating frequency, and<inline-formula> <tex-math notation="LaTeX">10\times </tex-math></inline-formula> compression ratio on <inline-formula> <tex-math notation="LaTeX">256\times 256 </tex-math></inline-formula> DVS pixels. |
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| Bibliografia: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2573-9603 2573-9603 |
| DOI: | 10.1109/LSSC.2024.3375110 |