Search Results for author:
Found 9 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.
A Context-Switching/Dual-Context ROM Augmented RAM using Standard 8T SRAM
The landscape of emerging applications has been continually widening, encompassing various data-intensive applications like artificial intelligence, machine learning, secure encryption, Internet-of-Things, etc.
Neuromorphic-P2M: Processing-in-Pixel-in-Memory Paradigm for Neuromorphic Image Sensors
Edge devices equipped with computer vision must deal with vast amounts of sensory data with limited computing resources.
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.
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).
HYPER-SNN: Towards Energy-efficient Quantized Deep Spiking Neural Networks for Hyperspectral Image Classification
However, the accurate processing of the spectral and spatial correlation between the bands requires the use of energy-expensive 3-D Convolutional Neural Networks (CNNs).
Computational Efficiency
Hyperspectral Image Classification
+1
