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Found 8 papers, 0 papers with code
Technology-Circuit-Algorithm Tri-Design for Processing-in-Pixel-in-Memory (P2M)
The massive amounts of data generated by camera sensors motivate data processing inside pixel arrays, i. e., at the extreme-edge.
In-Sensor & Neuromorphic Computing are all you need for Energy Efficient Computer Vision
Although the overhead for the first layer MACs with direct encoding is negligible for deep SNNs and the CV processing is efficient using SNNs, the data transfer between the image sensors and the downstream processing costs significant bandwidth and may dominate the total energy.
Enabling ISP-less Low-Power Computer Vision
However, direct inference on the raw images degrades the test accuracy due to the difference in covariance of the raw images captured by the image sensors compared to the ISP-processed images used for training.
IRIS: Integrated Retinal Functionality in Image Sensors
Neuromorphic image sensors draw inspiration from the biological retina to implement visual computations in electronic hardware.
P2M-DeTrack: Processing-in-Pixel-in-Memory for Energy-efficient and Real-Time Multi-Object Detection and Tracking
The designs also reduce the sensor and total energy (obtained from in-house circuit simulations at Globalfoundries 22nm technology node) per frame by 5. 7x and 1. 14x, respectively.
Toward Efficient Hyperspectral Image Processing inside Camera Pixels
Hyperspectral cameras generate a large amount of data due to the presence of hundreds of spectral bands as opposed to only three channels (red, green, and blue) in traditional cameras.
P2M: A Processing-in-Pixel-in-Memory Paradigm for Resource-Constrained TinyML Applications
Visual data in such cameras are usually captured in the form of analog voltages by a sensor pixel array, and then converted to the digital domain for subsequent AI processing using analog-to-digital converters (ADC).
Intrinsic Spike Timing Dependent Plasticity in Stochastic Magnetic Tunnel Junctions Mediated by Heat Dynamics
Neuromorphic computing aims to mimic the behavior of biological neurons and synapses using solid-state devices and circuits.
