3-D In-Sensor Computing for Real-Time DVS Data Compression: 65-nm Hardware-Algorithm Co-Design - Nair, Gopikrishnan R.; Nalla, Pragnya S.; Krishnan, Gokul; Anupreetham; Oh, Jonghyun; Hassan, Ahmed; Yeo, Injune; Kasichainula, Kishore; Seok, Mingoo; Seo, Jae-Sun; Cao, Yu (IEEE solid-state circuits letters, 2024)
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 10× compression ratio on 256× 256 DVS pixels.