Temporal frame filtering for autonomous driving using 3D-stacked global shutter CIS with IWO buffer memory and near-pixel compute
With the advancement of deep learning to solve autonomous driving problems, the
computation and memory requirements have been growing rapidly. Near-pixel compute …
computation and memory requirements have been growing rapidly. Near-pixel compute …
Optimization strategies for digital compute-in-memory from comparative analysis with systolic array
Compute-in-memory (CIM) is a promising solution to accelerate the extensive multiply-and-
accumulate (MAC) operations in deep neural networks (DNNs). Although mixed-signal or …
accumulate (MAC) operations in deep neural networks (DNNs). Although mixed-signal or …
Design and Thermal Analysis of 2.5 D and 3D Integrated System of a CMOS Image Sensor and a Sparsity-Aware Accelerator for Autonomous Driving
For the autonomous driving application, data movement has increased rapidly between a
CMOS Image sensor (CIS) and the processor due to increase in image resolution. Advanced …
CMOS Image sensor (CIS) and the processor due to increase in image resolution. Advanced …
Thermal modeling of 2.5 D integrated package of CMOS image sensor and FPGA for autonomous driving
Deep learning algorithms for autonomous driving require significant data movement
between the camera and the processor. We propose using 2.5 D integration of a CMOS …
between the camera and the processor. We propose using 2.5 D integration of a CMOS …
Impact of In-Pixel Processing Circuit Non-idealities on Multi-object Tracking Accuracy for Autonomous Driving
Deep learning algorithms are robust to a small amount of noise in the input image.
Traditionally, image signal processors (ISP) are used with the CMOS image sensor (CIS) to …
Traditionally, image signal processors (ISP) are used with the CMOS image sensor (CIS) to …